WiMAX Made Simple

SYSTEM PERFORMANCE VARIABLES

2012-01-24T06:54:00.000-08:00

We begin by presenting our metrics results in a comparative way, to show that they can give a good insight into the model performance. In Figure 1 we show the four previously defined performance metrics for the scenario with low population density and flat terrain and with low traffic requirements. We can see that the percentage of connected users increases with the the number of sites. There are additional connected users with relay topology and even more with mesh topology. We can also see that the increase in the number of sites reduces the number of users per base station, i.e., reduces efficiency. The percentage of frame occupancy shows that as the number of sites increases, every base station has a lower number of users connected to it and this causes a lower frame occupancy. With many base stations, the frame occupancy is really low. We can see that a mesh solution has a higher occupancy up to four active sites. This is because it increases the number of connected users but consumes more resources because of multihop links.

Figure 1: Results comparison for low density—flat scenario and low traffic requirements.

The equivalent transmission profile clearly shows that a PMP topology has a higher spectrum efficiency than a mesh or relay solution. This is because multihop topologies require more capacity due to multiple hops. In PMP topology this parameter remains almost constant, which means that the addition of new base stations allows the inclusion of more users but does not improve link quality. We analyze that this is caused by the reduction of the transmission power of some base stations due to interference restrictions. The increase of link efficiency with the number of sites for mesh and relay topologies, means that users reduce the number of hops to reach the nearest base station. The similarity between mesh and relay topology means that with many active sites, there are paths with at most two hops for some users. As the number of sites is higher, there is a low use of multihop links.

Automatic and Optimized Cell-Mesh Planning in WiMAX RESULTS

2012-01-20T07:13:00.000-08:00

Our goal is to define the best conditions to cover rural remote users by the usage of PMP and multihop topologies. We defined four scenarios with different population density and topographical condition. We also defined three different set of values for traffic requirements classified as low, medium, and high requirements, presented in Table 1. Finally, we included in the results PMP, relay, and mesh topologies.

Table 1: Traffic Parameters for Different Requirements
Traffic Requirements	R_granted	R_voice	R_be	ρ
Low	20,000	24,000	10,000	0.025 Erl
Medium	60,000	24,000	40,000	0.025 Erl
High	120,000	24,000	80,000	0.050 Erl

We use the following metrics to deeply analyze the performance of the solutions found by the optimization algorithm and compare the different topologies considered.

as previously defined.
The ratio between the number of connected users and the number of used base stations (|C_u|/|B_u|). This is the value previously defined before normalization.
Average frame occupancy percentage: This variable determines how much of the frame is occupied on every base station. This variable is important to measure if a multi-point solution can improve a PMP solution.
Equivalent modulation and coding schema: In WiMAX standard [22], there are several levels of transmission profiles defined, ranging from 1/2 BPSK to 3/4 64QAM. Every transmission profile is defined by the coding factor and the modulation factor. We define a quantity by the product of these two values, equivalent to the amount of bits that are sent within one QAM symbol into an OFDM symbol. The lowest value is 0.5 and the higher value is 4.5. We calculate the average value for all the users connected to every base station. An average value near 4.5 means that the solution has a high spectrum efficiency, equivalent to say that all users have the best link conditions possible.

In Figure 1, we present a solution for the low population density and flat terrain scenario with PMP topology. Dark triangles represent sites location. Each antenna in every site is represented by its radiation pattern with a black line indicating its orientation. The connection from users to base stations is represented by a solid line with the same color of the radiation pattern. Information text near the base station indicates the site index, the frame occupancy percentage, and the base station transmission power. In Figure 2 we present a mesh topology solution for the same terrain shown in Figure 1. Every site includes all the base stations that were used on the PMP solution. The information about each site is the site index and the average frame occupancy percentage of all base stations used. Gray curves represent terrain heights.

Figure 1: Example solution for a PMP topology.

Figure 2: Example solution for a mesh topology.

In the following we discuss the behavior of each one of the performance metrics with respect to the number of sites. We focus then on the number of users that can connect to the system as a function of traffic requirements and the terrain characteristics for all three topologies. We finally discuss the optimization objective function. We found interesting to split multihop solutions into relay and mesh, because when the mesh solution performs near the relay solution, it means that the relay solution as proposed in study group IEEE 802.16j could be enough over a full mesh solution.

OPTIMIZATION AND CELL PLANNING MODEL

2012-01-17T07:08:00.000-08:00

The second problem is to find the set of active base stations, their orientation, and transmission power to achieve the optimum coverage and capacity assignment to users. The problem is separated into two parts.

1 Transmission Towers Construction

We define the concept of Transmission tower as a fixed set of active base stations placed at one active site. One site can have many transmission towers but only one of them can be active. We try to explore different alternatives for the number of antennas, their transmission power, orientation, and radiation pattern. After we build them, the problem reduces to choose one of them from every available active site.

The process begins with a set of candidate sites. We discard candidate sites with very low coverage. Also, if there are two sites with similar coverage, we discard the one with the lowest coverage. To build the transmission towers at one site, we begin placing one omni-directional antenna with the maximum transmission power. If this base station is not saturated, i.e., all covered users can connect to it, then we create several transmission towers with one single antenna and different transmission powers, chosen from a set of discrete values. We use also 120° and 180° sectorized antennas.

On the other case, if the first omni-directional antenna base station is saturated, i.e., not all covered users can connect because of capacity restrictions, then we build a set oftransmission towers composed of several antennas with 120° and 180° sectors. We use all possible combinations of transmission power and sectors to build several options for the site. We solve coverage and capacity assignment by previously described algorithms to find the orientation of each set of active base stations. We finally remove redundanttransmission towers from the set of available ones. We solve this for every site to get a set of transmission towers and a matrix that keeps a record of the users that connect to each one of them. This information is used in the optimization process.

2 Optimization Process

In this process, we try to find the set of active transmission towers to optimize coverage and connect the highest number of users. We fix the number of sites during one execution of the algorithm to find the best solution. After that, we increment the number of sites and run the algorithm once again. At the beginning, we start from an empty solution, then we try to improve it by activating, deactivating, or moving transmission towers. We do this every iteration using a probabilistic model to decide if a new solution is chosen or not over the current one. However, we keep track of the best solution that has been reached so far. The iterative process has two main components:

Building of a new solution: In this process, we start from the current solution and try to improve it by a randomly chosen modification. Our modifications are based on those presented in Ref. [20]. We can deactivate any active transmission tower and activate any inactive transmission tower. We deactivate an activetransmission tower randomly by assigning a deactivation probability inversely proportional to the number of users connected to it. We activate a new transmission tower randomly by assigning an activation probability proportional to the number of uncovered users that could connect to it. A new solution is analyzed for a feasible channel assignment by trying several combinations to reduce interference. We fix the number of available channels. We finally use the channel combination that has the lowest interference level, represented by the highest number of connected users.
Optimization process: This algorithm iterates, comparing the new candidate solution and the current solution. This is the core process for simulated annealing, in which a new candidate solution replaces the current solution according to the improvement and the temperature of the system. If the candidate solution is better than the current solution, it is accepted. Otherwise, it has an acceptance probability that depends on how bad is the new solution with respect to the current solution and the temperature of the system. This process keeps track of the current solution and the best solution ever found.

The metric used to decide the performance of a solution is the percentage of users that could connect to any base station. It means that a better solution has more connected users than a previous one. We must recall, that other optimization criteria are included in the inner process.

Interference is reduced during the building of a candidate solution by choosing the lowest interference channel assignment, i.e., each new candidate solution tries to increase the number of connected users with the minimal interference.
For the iteration process we add and remove base transmission towers to the new candidate solution. If we have two solutions with similar number of connected users, we choose the one with the lowest number of base stations. This way we reduce the cost related to the number of base stations.
The QoS guarantees are included in the User-Base station assignment model, where we try to connect users to base stations according to their spectrum efficiency. Also, in the Capacity assignment model, if we connect a user to a base station, we guarantee that the requirements are satisfied.

PROBLEM DESCRIPTION | Automatic and Optimized Cell-Mesh Planning in WiMAX

2012-01-13T04:46:00.000-08:00

We look for the optimum conditions required by a fixed broadband wireless access system to cover remote rural users. We extend cellular automatic cell planning models to build an automatic cell planning tool. Our goal is to design a system to provide access to remote rural users under realistic conditions considering data networks. And also to find out how multihop topologies can improve over PMP. We describe these issues in the following.

1 SCENARIO DESCRIPTION

We suppose a set of potential users, which are placed on real villages and country houses. Users are not necessarily uniformly distributed. We suppose that all the users have the same traffic requirements, they are fixed and have an external energy source. Every region corresponds to a real place in Colombia.

High population density, flat terrain: This scenario represents a city with uniformly distributed users, with shadowing caused by surrounding obstacles. One base station covers many users and usually operates saturated. There are also usually several base stations on the same site.
Medium population density, medium mountainous rural region: This scenario represents a typical rural region, where some of the users are uniformly distributed and some of them are placed on small towns or near roads or trails. We suppose that some of those users cannot be easily covered because of nearby obstacles.
Medium population density, mountainous rural region: We suppose a user distribution similar to the previous scenario. We suppose the existence of high mountains and rivers that cause deep canyons. There are several users with difficult coverage conditions, i.e., there are no privileged places that can cover a high percentage of the region.
Low population density, flat terrain: We suppose users widely separated from others. This is common in regions dedicated to agriculture, pasture lands, and forestry. In this case the main problem is caused by the long distance links. We also suppose some places with higher population concentration over the region average such as small villages.

2 DATA MODELS

Data models differ from voice systems in many ways. There are different QoS requirements, they are based on packet multiplexing and there are different transmission schemas that depend on link quality. QoS requirements for data networks include several criteria such as delay, delay variation, and guaranteed data rates. Base stations make use of statistical multiplexing to increase system capacity. An analysis of different transmission flows and the resources assignment problem. The base stations perform a process known as packet scheduling to assign transmission opportunities to packets. Some packets can have priority over others, to allow transmission of more urgent packets. Schedulers and multiplexing models for data traffic are difficult to use in the design process. Data networks like WiFi and WiMAX support AMC. As users have different spectrum efficiency values, they might require different number of slots on transmission frames to achieve the same data rate.

3 DIFFERENT TOPOLOGIES TO SOLVE THE PROBLEM: PMP, MESH, RELAY

In PMP, a user connects to a single base station using a direct link. It chooses which base station to connect to from a set of available base stations, depending on link quality and available capacity. In multihop networks, information can go through several links until it reaches the base station. Packets transmitted through multiple hops have higher delay and require more capacity, i.e., multihop topologies extend coverage at the expense of more capacity consumption. Operation of multihop networks makes use of spatial reuse, controlled by a scheduler. Two different links on the same channel can transmit simultaneously if they do not interfere with each other. Our assumption is that there is only one active link among all links belonging to paths that end on the same base station, but links of users connected to different base stations can be active simultaneously even though they use the same channel.

In multihop topologies, users must decide not only which base station to connect to, but also the path that the packets should follow. The amount of resources required at every hop is not the same, as different links can have different modulation and coding schema. In our case, a certain node chooses the route that requires the lowest amount of resources. We limit our problem to routes up to two hops in relay topologies and up to five hops in mesh networks. A larger number of hops would be prohibitive in terms of delay and resources consumption.

AUTOMATIC CELL PLANNING

2012-01-10T07:05:00.000-08:00

Wireless design has a high complexity because of the random characteristics and the shared nature of wireless medium. A cell planning example based on WiMAX standard, even though it is not based on automatic cell-based planning. A complete synthesis of automatic cell planning process is presented. The model uses a multiobjective function, built by a weighted sum of functions, each one representing signal coverage, capacity, system growth capabilities, and cost. The decision variables are channel assignment, sites location, and transmission power. Because of the nonlinear characteristics of this model, author uses genetic algorithms to solve it.

There is a general description of the optimization problem related to wireless network design. It presents a simplified model for global system for mobile communications (GSM) cell planning based on the activation or deactivation of a set of candidate base stations. There are other models based on multiobjective functions. Authors solve the problem by iteratively changing the transmission power used by base stations to guarantee signal reception and interference reduction. They use heuristic techniques and artificial intelligence algorithms in the solution process. Automatic cell planning based on artificial intelligence algorithms. Makes a comparison among different techniques, showing a better performance of tabu search over the other techniques. The variation of the height of antennas and the transmission power by using genetic algorithms, but they do not consider capacity criteria. Particle swarm optimization is used with an optimization criteria similar to that.

Two heuristic techniques to solve high complexity nonlinear optimization problems are tabu search and simulated annealing . Use tabu search to solve an integer linear programming problem. A design process which is similar to ours. It uses simulated annealing to choose active base stations from a set of candidate base stations. A similar problem is solved using simulated annealing too.

Previous references are oriented to cellular networks to provide voice services. WiMAX networks support different adaptive modulation and coding (AMC) schemas according to link quality. It also defines different types of connections ranging from a circuit-like access to a completely random access. In WiFi networks, there are different link conditions as in WiMAX, but there are not different types of flows. Most of the references for the design of WiFi networks, use the position of access points, their transmission power, and the channel assignment as the decision variables.There is a simple but illustrative description of the problems involved in wireless LAN design. A genetic algorithm to solve channel assignment in Wireless LAN Networks.The algorithm modifies transmission power of fixed access points to react to changes in user traffic requirements. The model described uses a heuristic search model to provide coverage and a minimum data rate at test points. Solve the joint problem of access points location and channel assignment.There is a good description of Wireless LAN Network planning. They use a penalty function to avoid placing access points near each other, to increase the probability of a posterior feasible channel assignment solution. The objective function is a weighted sum of a coverage variable, an interference mitigation variable, and a QoS variable. Authors use tabu search to solve the problem.

Consider the problem of locating relay nodes to improve access point covered area. The decision variables are user nodes and relay nodes location. PMP and multihop topologies can be mixed, where an IEEE 802.16 wireless mesh network interconnects a cell-based IEEE 802.11 access network. In more dynamic scenarios, the design process must prepare a feasible scenario for operation. Discuss several issues for channel allocation and transmission scheduling and a connection admission control and a transmission power control for WiMAX networks is presented.

SITE DEPLOYMENT AND CONFIGURATION EXPERIMENTAL RESULTS

2012-01-06T00:00:00.000-08:00

We start with an experiment on site selection. There are 55 candidate sites as shown in the left of Figure 1. Each site has three cells.Without loss of generality, it can be assumed that all the cells have the same configuration as given in Table 1. The configuration of subscriber station is given in Table 2.

Figure 1: Site deployment before and after optimization.

Table 1: Cell Configuration
Parameter	Value
TX power (dBm)	40
Operating frequency	2350
Bandwidth (MHz)	10
Antenna gain (dBi)	18
Antenna 3 dB angle	65°
Antenna height (m)	40
Receiver sensitivity (dBm)	−96
Noise figure (dB)	4

Table 2: Configuration of Subscriber Station
Parameter	Value
TX power (dBm)	23
Antenna gain (dBi)	0
Antenna height (m)	1.5
Receiver sensitivity (dBm)	−100
Noise figure (dB)	7

Inhomogeneous traffic distribution is assumed. Furthermore, we assume that all the candidate sites have equal cost in terms of CAPEX and OPEX. The target is to cover 95 percent of the planning area and 98 percent of the predicted traffic.

The site selection method is applied in this experiment. The obtained result is displayed in the right hand side of Figure 1, which consists of 19 sites (57 cells). This design is (locally) optimal in the sense that one cannot remove a site without violating the coverage constraint, one cannot increase coverage by replacing a site by another site, and one cannot replace two sites by another site without violating the coverage constraint. This experiment shows that significant reduction of deployment cost can be achieved by using the site selection technology.

In the next step, this design with 19 sites (57 cells) is further optimized by applying the advanced search algorithm. The optimization is carried out by changing cell parameters including antenna tilt, azimuth, and TX power to improve the network performance. A main performance measure is SINR. Figure 2 shows the SINR CDF (Cumulative Density Function) of different optimization scenarios.

Figure 2 SINR CDF of different designs.

Optimization Process | AUTOMATIC CELL CONFIGURATION

2011-12-25T00:12:00.000-08:00

The basic structure of the local search algorithm that has been developed for an optimization of WiMAX networks is depicted in Figure 1.

Figure 1: Local search algorithm for WiMAX network optimization.

The algorithm comprises the basic elements of a local search method that have been presented in the previous section. The local search starts from an initial solution, which can for example be the current configuration of the network to be optimized, a manually planned solution, or a solution suggested by the fast heuristics presented in the previous section.

At the beginning of each search step, the search neighborhood is generated. At first a cluster of cells is selected for which parameter changes are considered. Based on the selected cells the search neighborhood is generated and explored to yield a new solution.

The quality of a certain solution is assessed by a performance analysis. The choice of the method depends on the particular application and is a trade-off between accuracy and speed of the optimization process. From the results of the performance analysis, a cost value is generated by means of a cost function. The cost function is basically a linear combination of the evaluated quantities. In addition, penalty components are added if certain thresholds are exceeded for any of the different cost function components (e.g., coverage probability below some design target). The search process can be guided by appropriately setting weights for the different cost function components.

As a search paradigm either a descend method or a Tabu search can be applied. The Tabu list is maintained independent of the selected search paradigm. The search paradigm only influences the way in which the search is terminated. In case of the descend method, the search process is terminated if no improvement could be found. For the Tabu search, nonimproving moves are accepted to escape from local minima. The Tabu search is terminated once no improving moves were found for a certain number of search steps.

The performance of the local search method strongly depends on the applied performance evaluation. The choice between the methods is a trade-off between accuracy and running time. The basic static method is the fastest but as was shown has some weaknesses in terms of accuracy of results. The presented statistical methods significantly outperform the latter method in accuracy of results but even if implemented efficiently are of higher computational complexity, especially if the experimental analysis is applied for evaluating quantities per pixel. The local search optimization presented in this section can be extended to yield a hybrid method which makes use of two methods for performance evaluation. The exploration of the neighborhood is split into two parts. In a first step, the neighborhood is explored by the use of a simple and fast basic performance evaluation method. As a result a list of candidate solution is generated. The list is sorted with respect to cost values. The next candidate solution is selected from this list using a more accurate but also more time consuming advanced performance evaluation. Either the first improving solution from the list or the best solution from a subset of most promising solutions (“short list”) is selected.

Advanced Search Algorithm | AUTOMATIC CELL CONFIGURATION

2011-12-21T07:07:00.000-08:00

The optimization method described in the previous section is well suited for an initial planning and in cases where the level of completeness or accuracy of input data is limited. For a detailed optimization that takes the full set of input data into account, a search approach is proposed. That is, the space of possible configurations denoted as search space is explored to find the point in the search space that is optimal with respect to a certain criterion. This point is denoted as global optimum. Exhaustive search traverses the complete search space in a systematic manner. As all points in the search space are visited, with exhaustive search it is guaranteed to find the global optimum. The search space is very large for typical applications in network planning. For each site there can easily be several hundreds of possible configurations. Furthermore, configurations of different sites cannot be considered independently, so that the amount of possible network configurations grows expotentially with the number of sites. Hence targeting this area, an exhaustive search is too time-consuming and local search algorithms are commonly used for network optimization purposes.

Local search algorithms start at some point in the search space denoted as initial solution and subsequently move from the present to neighboring solutions, if they fulfill some criterion, i.e., appear to be better or more promising. Local search algorithms cannot guarantee to find the global optimum. The objective of the search algorithm—developed or tailored for a particular problem—is to find a solution that is at least close to the global optimum. Local search algorithms are thus often classified as heuristics.

The basic procedure of local search is independent of the actual search algorithm applied. Starting from the initial solution in each search step, first a search neighborhood is generated. The search neighborhood is a subset of points from the search space that are close to the current solution, i.e., that have some attributes in common with the current solution. The point that is most appropriate with respect to some criterion is selected from the search neighborhood and accepted as new initial solution for the next search step. If no appropriate new solution is found (or some other stop criterion is fulfilled) the search is terminated. The comparison of points from the search space is carried out by means of cost values associated with them. The cost values are generated from a cost function, in the literature often also referred to as objective function. The objective function maps a given point in the search space to a cost value. The cost value can be a scalar but could also be represented by a vector. In the latter case, an appropriate function to compare cost values needs to be defined.

Local search algorithms are very much application specific. However, several search paradigms have been developed in the last three decades. The simplest search paradigm is the descent method. This method always selects the solution from the neighborhood that has lowest cost. If this value is lower than the lowest value in the last search step, the solution is accepted as a new solution, otherwise the algorithm is terminated. The algorithm hence explores the search space by always moving in the direction of the greatest improvement, so that it typically gets trapped in a local minimum. A local minimum is a solution that is optimal with respect to the search neighborhood but which generally is worse than the global optimum. To escape from local minima, among several others, one widely applied approach is to carry out restarts, that is, the local search is restarted from a new solution that is selected from a different area of the search space. The new start solutions are often selected randomly. If restarts are applied, the algorithm strictly speaking is not a local search algorithm anymore.

Another option for escaping from local minima is to accept also the cost deteriorating neighbors under certain conditions. The most prominent local search paradigms that apply this strategy are simulated annealing and Tabu search.

Simulated annealing is based on an analogy with the physical annealing process. In simulated annealing improving points from the neighborhood are always selected when exploring the search neighborhood, nonimproving points are accepted as new solutions with a certain probability. The probability of acceptance is a function of the level of deterioration, but also gradually decreases during the algorithm execution. The reduction of the probability of acceptance is determined by the cooling scheme.

In contrast to the simulated annealing which comprises randomness, classical Tabu search is deterministic. The basic operation is equivalent to the descent method with the difference that the best point from the neighborhood is also accepted if it is worse than the current solution. In this way the search is directed away from local minima. To avoid a move back to already visited solutions, a Tabu list is introduced. The Tabu list typically contains sets of attributes of solutions that have already been visited. If a point from the neighborhood exhibits one of the sets of attributes stored in the Tabu list, the point is only accepted as new solution if its quality, i.e., cost, exceeds a certain aspiration level. The Tabu list is updated, keeping the individual entries only for a number of iterations. The size of the Tabu list is a very important design parameter of the Tabu search, it in particular needs to be chosen large enough to prevent cycling, but a too large list might introduce too many restrictions. Several enhancements to the basic operation of the Tabu search have been introduced most of which modify the handling of the Tabu list. These include intensification and diversification schemes

Optimization Parameters and Targets | AUTOMATIC CELL CONFIGURATION

2011-12-17T08:56:00.000-08:00

Optimization Parameters and Targets

The targets of the radio network optimization are mainly twofold. First target is to minimize the interference caused by the individual cells, while a sufficient coverage over the planning area is maintained. This is in general a trade-off and needs to be balanced, e.g., tilting down the antenna causes lower coverage, but also lower interference in neighboring cells and thus a potentially higher network capacity. Second target is the traffic distribution between cells. It is desirable to maintain similar cell loading of neighboring cells to minimize blocking probabilities and to maximize spare capacity for traffic fluctuations and a future traffic evolution.

The most effective parameter in network optimization is the antenna tilt. Antenna tilts need to be set such that the traffic within the “own” cell is served with maximum link gain, but at the same time the interference in neighboring cells is minimized. The possible tilt angles are typically restricted because of technical and civil engineering reasons. Especially in case of collocated sites with multiband antennas there might be strong restrictions on the possible tilt angles to be taken into account during optimization.

The transmitted pilot channel power and the other common channel powers, which are typically coupled by a fixed offset, are also vital parameters of network optimization. It needs to be assured that these channels are received with sufficient quality by all users in the serving cell. At the same time a minimization of the common channel powers yields significant capacity gains: Firstly, additional power becomes available for other (user traffic) channels, and secondly, the interference is reduced. The gains obtained from reducing the pilot power are often underestimated. It is important to note that in a capacity-limited WiMAX network (e.g., in urban areas) the reduction of pilot power levels by a certain factor also reduces the total transmit power of cells and as a consequence the cell loading by up to the same factor.

Optimization of azimuth angles of sectored sites is of great importance in particular in case of antennas with rather small horizontal beam-width (e.g., 65° vs. 90° in case of three-sectored sites). In this case the difference between antenna gains in direction of the main lobe and the half-angle between neighboring sectors is comparatively large, and cells of neighboring sites might need to be adjusted such that maximum coverage is achieved. It is observed that during optimization azimuth changes are in particular introduced to reduce coverage problems. For possible azimuth angles typically even stronger restrictions apply than for the tilt angles.

The antenna height is also often a degree of freedom for the optimization. Higher antennas can provide better coverage, but on the other hand also cause more interference in neighboring cells. Additional important parameters are the antenna type and the number of deployed sectors at a site. Both parameters are closely coupled, as a larger number of sectors also suggest the use of antenna pattern with smaller horizontal beam-width. The choice of sectorization is typically a trade-off between increased network capacity and higher monetary cost.

SITE SELECTION | SITE DEPLOYMENT AND CONFIGURATION

2011-12-13T03:05:00.000-08:00

The site selection targets at optimizing mobile radio networks. It provides flexible methods to modify the configuration of a network in such away that key performance measures are optimized and pivotal performance targets are met.

The working engine behind the site selection is a fast construction and solution of downlink or uplink cell equations for a given network configuration from which most performance measures can be deduced. Based on an analysis of the “permitted configurations,” a preprocessing tree is constructed which contains all partial coefficients of the cell equations which might be relevant for the construction of the downlink/uplink/combined cell equations of an arbitrary permitted network configuration. This preprocessing tree then allows for an efficient accumulation of the coefficients of the equation system which in turn yields a fast calculation of the cell transmit/interference powers/activities from which most performance measures can be deduced. One can observe how many configurations are evaluated by looking at the number of evaluations in the status messages (which are issued about once in a second) when the algorithm is running after preprocessing.

Based on this fast evaluation, an optimization of the network is performed by evaluating all neighboring configurations of the current configuration and moving to the best of them if this improves the current network configuration, where the neighboring configuration deviates from the current configuration with short distance in the searching space. As long as an improvement can be obtained this optimization step is iterated. When no improvement can be found the last configuration is stored as the end design. On top of this basic procedure (construct preprocessing tree—perform descent to local minimum), there is the possibility for a repetition (since when many sites are switched off the coefficients in the preprocessing tree might become more and more irrelevant hence yield too conservative cell equations) and for a division of large problems into smaller ones, where one part of the network is optimized after another in a circular way.

A key role in the simple local descent iteration is played by the comparison operator between configurations. Here the algorithm allows choosing the relevant performance measures/constraints by assigning priorities to them. For example, one often assigns the monetary cost the highest priority and the coverage constraint the second highest (nonzero) priority.

The site selection can be used for optimizing cell parameters and selecting sites/cells. It can start from given configurations or empty/full networks. It can look through neighborhoods with selectable depths and iterate through these neighborhoods with a deterministic steepest or gradient descent, or a greedy random descent. However, this flexibility is at the price of a more complex user interaction. Through a large number of parameters the user has to tell the program what kind of optimization is intended. In addition, in large optimization problems, the preprocessing tree requires a huge amount of computer memory and if a large number of partial interferer coefficients are stored the evaluation can become slow. Partial coefficients which cannot be stored are accumulated in a so-called remainder coefficient. This remainder coefficient can accumulate a large number of small interferer and then makes sure that these interferers are not neglected in the cell equations. This saves computation time and computer memory, but might yield overconservative cell transmit powers/channel activities, when the number of stored coefficients becomes too small or when the interferer which belong to the stored coefficients are not active (e.g., is switched off). In these cases one might have to recalculate the preprocessing tree or allow for a larger number of coefficients within the nodes of the tree. Or one could divide a large problem into smaller ones which also reduces the preprocessing tree.

1 Candidate Sites and Permitted Configurations

The site selection algorithm starts from a description of the permitted network configurations. Such descriptions consist of a set of potential cells (configured cells which might appear in a network design) and subsets of these cells which we call selection sets and choice sets.

The meaning of a selection set is that all potential cells in this set have to appear together in a permitted network configuration or none of them. In a site selection, problem one can think of a selection set as the set of cells which belong to a site: One has to select all of them or none of them. Since selection sets can consist of a single cell, one can also define “cell selection” problems, where individual cells are selected by putting each potential cell into an individual selection set.

The meaning of a choice set is that exactly one of the potential cells in this set has to appear in a permitted network configuration. In a tilt choice problem such a set would consist of all potential cells which model the same cell but with different tilt settings. Defining such a choice set means to tell the optimization kit that it should choose exactly one of these cells for a solution design. Through a choice set which contains only one potential cell one can tell the program that this cell must appear in a solution design.

2 Optimization Algorithm

Given this definition of permitted configurations and the results of the preprocessing, the algorithm can walk through the permitted configurations and try to improve the performance measures of interest during this walk. The permitted configurations are the possible search choices for the optimization algorithm. The user can choose between several different manners of walking towards an optimized configuration by either “local steepest descent,” “local gradient descent,” “random greedy descent,” or “local immediate descent.”

The local steepest descent method evaluates all neighbor network configurations and then replaces the current with the best one found, where the comparison checks the selected performance measures exactly according to their priority.

The local gradient descent method evaluates all neighbor network configurations and then replaces the current one with the best one, where the comparison is based on gradients instead of the absolute values of the performance measures.

The random greedy descent method chooses randomly one new configuration in the neighborhood of the current one and moves to it if it performs better than the old one.

The local immediate descent is an exotic procedure where improvements are immediately accepted and the search continues from the improved configuration. This can speed up the algorithm, but at the cost of perhaps not giving as good results as the above more thorough search methods.

In all cases the walk moves from the current configuration to a configuration within a certain neighborhood of the current one. The neighborhood is defined as all configurations which can be reached by a given number of permitted elementary steps. An elementary step is either changing a given permitted configuration by switching one selection set on or off (adding its cells to the configuration or removing them) or modifying one cell in a choice set (replacing it by another one of the same choice set).

However, increasing the search depth to more than one, usually dramatically increases the computation time because a large number of configurations might have to be considered before accepting a move. This increase can be controlled through algorithm parameter.

3 Performance Evaluation and Comparison

There are two groups of performance measures: Ordinary performance measures and performance constraints, which additionally have a target value which should be reached. The ordinary performance measures are

ActiveCells: The number of active cells in the evaluated network configuration. (Can be used instead of TotalFirstYearCost if detailed costs are irrelevant.) Small values are better than large ones.
ActiveSites: The number of active sites. (Can be used instead of TotalFirstYearCost if the detailed costs of the base stations are irrelevant.) Small values are better than large ones.
MaxOtherCoefficient: The maximum of the nondiagonal coefficients in the coupling matrix. Small values are better than large ones.
MaxOwnCoefficient: The maximum diagonal coefficient in the coupling matrix. Small values are better than large ones.
MaxSiteCostPerAccess: The maximum first year cost of one BS location (including hardware) divided by its sum of area and traffic access coverage in percent. Small values are better than large ones.
MaxUserLoadInPercent: The maximum user load of a cell in the network. Small values better than large ones. If this measure has high priority, the optimization seeks for a configuration in which the maximum user load of a cell is as small as possible.
OverloadedCells: The number of overloaded cells. Small values are better than large ones.
TotalFirstYearCost: The most important (nontechnical) performance measure in site selection, namely the total first year cost of a given network configuration. Small values are better than large ones.

The performance constraints are

AreaCoverageInPercent: The percentage of the area which can be served (i.e., considered pixels which have a large enough receive signal strength from at least one of the active cells) by the network configuration. Its target value is the area coverage defined in the underlying optimization profile. Large values are better than small ones.
AccessCoverageInPercent: The number of users on the served area of a configuration as a percentage of the number of users in the total considered area. The target value is the access coverage from the underlying optimization profile. Large values are better than small ones.
MinCoverageGapInPercent: Here one looks at the differences of the area coverage in percent and its target value and the traffic coverage in percent and its target value. The smaller one of these two differences is the MinCoverageGapInPercent. Large values are better than small ones. The target value of this constraint is 0.
TrafficCoverageInPercent: After solving the cell equations one can assess the number of customers which can be served by the network. The target value is the traffic coverage from the underlying optimization profile. Large values are better than small ones.

Only performance measures are calculated and displayed during the optimization for which the priority is strictly positive. The one with the largest positive value has highest priority, the one with the lowest positive value the lowest priority. The positive priorities should be different.

When two evaluations of network configurations are compared, first the number of violations (i.e., number of performance constraints, where the target is not met) is compared. The configuration with fewer violations is better than the other one.

Next, if both configurations have the same nonzero number of violations, the violation with the highest priority is considered. The configuration where the corresponding performance measure has a better value is considered as better than the other one. If both values agree, the performance measure with a violation and next highest priority is considered, and so on.

If there are no violations or the performance measures of all violated constraints agree, the performance measure with the highest priority is considered: The configuration which has the better value of this performance measure is considered to be better than the other one. If both values agree one looks at the performance measure with the second highest priority, and so on.

Since the steepest descent algorithm accepts only “better” permitted configurations, this evaluation first tries to reach the performance targets and then to optimize the high priority measures.

If the optimization method is set to local gradient descent, the decision whether a neighbor of the current configuration is better than another neighbor of the current configuration is slightly more complex: instead of the absolute values one considers gradients with respect to the current configuration.

WIMAX NETWORK PLANNING PROCESS

2011-12-07T06:36:00.000-08:00

WiMAX radio planning involves a number of steps ranging from tool setup to site survey. The process is similar to any wireless network. What differs between WiMAX and other technologies are the actual site configuration, KPIs, and the propagation environment as WiMAX may support mobile and fixed users where the latter may employ directional/rooftop antennas.

The final radio plan defines the site locations and their respective configuration. The configuration involves BTS height, number of sectors, assigned frequencies or major channel groups, types of antennas, azimuth and downtilt, equipment type, and RF power. The final plan will be tested against various KPI requirements mainly coverage criteria and capacity (or signal quality). Figure 1 can be used as a guide in developing a planning process. The planning process also largely depends on the planning tool used.

Figure 1: WiMAX radio planning process.

The planning process in Figure 1 includes measurements (i.e., drive test and verifications) after the site survey. This procedure is not mandatory for all sites if the site count is too large. Usually, site survey and the KPI analysis give an indication of which areas are expected to have poor RF quality and which sites are involved. This is usually done when the candidate site(s) are not located in ideal locations or if the site survey finds some discrepancies of the candidate(s).

The differences between WiMAX and 3G radio planning. WiMAX radio offers modest processing gain in a form of repetition coding and subchannelization. These features are only exploited when the signal quality demands more processing. To support high data rates, the radio plan must offer very good SINRs (signal to interference and noise ratio) even with very limited spectrum. For example, in the absence of subchannelization and repetition coding, the required SINR for the lower MCS (modulation and coding scheme) is around 5 dB and this needs to be achieved even with very tight frequency reuse factor of 1/3 or 1/4 in the presence of shadowing, where the reuse factor is the reciprocal of the number the cells using different frequencies and the sum of the frequencies presents the whole spectrum resource allocated to the planned system. Another consideration in the case of WiMAX planning is the high SINR requirements to support high data rates. Although a site is expected to support high data rates for CPEs closer to it, SINR values >30 dB are only possible in the absence of interference. This requires accurate modeling of the propagation and RF equipments. For example, in 3G, high data rates are possible even with C/(I + N) of < 10 dB as the processing gain enables the receiver to tolerate some amount of interference. In WiMAX, this is not the case as the processing gain is only provided through channel coding and limited coding repetition.

IMPACT OF THE RATIO OF THE COST OF BS TO RS ON SOLUTION

2011-12-03T11:00:00.000-08:00

It is also worth to notice how the ratio of the cost of BS and RS affects the site selection. Intuitively, as the ratio raising, the RS becomes relatively cheaper, so it should tend to select more RS compared to the BS and connections from TP to RS should increase. Figure 1 shows the trend. It shows number of connections between TP and BS and between TP and RS as the cost ratio varying from one to ten, i.e., from the cost of BS equals to the cost of RS to the cost of BS ten times the cost of RS. Figure 2 shows the corresponding average path loss between each TP and its communicating node. It can be seen that the path loss is decreasing which means the quality of the radio received becomes higher as the cost of RS becoming lower.

Figure 1: Average path loss between each TP and its communicating node as the ratio of the cost of BS and RS is varied.

Figures 2 and 3 show two extreme cases. In both cases, the number of candidate BS sites is 50, the number of candidate RS sites is 150, and the number of TP is 500. Figure 2 shows the plan of the cost of BS equals to the cost of RS. In this case, there are 38 BSs and 50 RSs being selected; 177 connections between TP and BS; 320 connections between TP and RS. Figure 3 shows the plan of the cost of BS ten times to the cost of RS. In this case, there are 28 BSs and 75 RSs being selected; 133 connections between TP and BS; 367 connections between TP and RS.

Figure 2: An output of the planning tool when the ratio of the cost of BS and RS is 1.

Figure 3: An output of the planning tool when the ratio of the cost of BS and RS is 10.

QUALITY, CAPACITY, AND ECONOMIC ISSUES OF NETWORK DESIGN

2011-11-30T15:00:00.000-08:00

The increasing demand for mobile communications leads mobile service providers to look for ways to improve the QoS and to support increasing numbers of users in their systems. Because the amount of frequency spectrum available for mobile communications is very limited, efficient use of the frequency resource is needed. Currently, cellular system design is challenged by the need for a better QoS and the need for serving an increased number of subscribers. Network planning is becoming a key issue in the current scenario, with exceedingly high growth rates in many countries which force operators to reconfigure their networks virtually on a monthly basis. Therefore, the search for intelligent techniques, which may considerably alleviates planning efforts (and associated costs), becomes extremely important for operators in a competitive market.

Cellular network planning is a very complex task, as many aspects must be taken into account, including the topography, morphology, traffic distribution, existing infrastructure, and so on. Things become more complicated because a handful of constraints are involved, such as the system capacity, service quality, frequency bandwidth, and coordination requirements. Nowadays, it is the network planner’s task to manually place base stations (BSs) and to specify their parameters based on personal experience and intuition. These manual processes have to go through a number of iterations before achieving satisfactory performance and do not necessarily guarantee an optimum solution. It could work well when the demand for mobile services was low. However, the explosive growth in the service demand has led to a need for an increase in cell density. This in turn has resulted in greater network complexity, making it extremely difficult to design a high-quality network manually [1–3].

Furthermore, OFDM (orthogonal frequency division multiplexing) technology is emerging as an attractive solution for fast wireless access. It has been adopted for many future wireless networks, e.g., FLASH (fast low-latency access with seamless handoff) OFDM and WiMAX. The UMTS (Universal Mobile Telecommunication System) evolution will go into the direction of OFDM, e.g., LTE (Long Term Evolution). Similar to other technologies, the deployment of OFDM networks poses the problem to select antenna locations and configurations with respect to contradictory goals: low costs versus high performance. A key to successful planning is the fast and accurate assessment of network performance in terms of the coverage, capacity, and QoS [4]. This also makes the conventional design methods insufficient for planning mobile networks in the future. Thus, more advanced and intelligent network planning tools are required. A promising planning tool should be able to aid the human planner by automating the design processes.

RADIO PLANNING OBJECTIVE

2011-11-29T08:35:00.001-08:00

The task of radio planning is to define a set of site locations and respective BTS (Base Transceiver Station) configurations with addressing the coverage and capacity figures derived from dimensioning. Dimensioning a new network/service is to determine the minimum capacity requirements that will still allow the GoS (Grade of Service) to be met. Site densities in each clutter type are one of the outputs. The site count, i.e., number of sites in a considered service area, derived in radio planning often differ from the site count derived from dimensioning since the actual site coverage may differ significantly from the assumed empirical model(s). There is always a risk that the planned site count may exceed the estimated site count from dimensioning. As a result several planning iterations are needed to reach a reliable figure.

One problem with radio planning deals with site density. Firstly, higher site density poses more difficulty in finding suitable candidates. This is true in all clutter types. In dense areas, most suitable sites are already overcrowded with 2G and 3G antennas. This will likely put the WiMAX antennas in less ideal positions. Secondly, there is a tendency that the candidate sites are not having comparable heights. This is a major drawback in radio planning because large differences in heights can distort the site dominance areas and cell ranges. The third problem is the bandwidth constraint which may require tighter frequency reuse. In this case, the radio plan must be as close as the ideal case.

Radio network planning normally follows the dimensioning exercise. Sometimes the dimensioning process includes a rough plan to justify the site count and coverage level using some commonly accepted propagation model and generic WiMAX system modules in the planning tool. In the actual planning phase, a number of inputs are needed to improve the quality and accuracy of the radio plan. Depending on the selected planning tool to use, a number of inputs maybe required to be fully utilized by the tool. For example, it is assumed that following items are already well considered:

Propagation characteristics of various areas (propagation models tuned)
Required inputs defined (clutter maps, terrain maps, building data, etc.)
Traffic and demographic information, i.e., per clutter type
WiMAX RF equipment parameters are defined (antennas, RF [radio frequency] features, etc.)
Options for BTS configuration (sectorized, omni, PUSC [partial usage of subchannels], FUSC [full usage of subchannels])
CPE (customer premises equipment) types and parameters defined (antenna types, mounting, diversity)

Two important decisions with regards to radio planning have to be considered prior to the actual planning exercise. Firstly, the level of accuracy when it comes to coverage and capacity needs to be considered and this highly depends on the accuracy of the propagation model in the planning tool. Secondly, the planner needs to decide how much RF optimization will be undertaken during the planning phase. This is only possible if the planning tool together with the planning parameters and equipments models are accurate enough. It is often the case where optimization is neglected during the planning process. Postplanning optimization exercise is often costly and produces only minor improvements. It is often limited to antenna adjustments (tilting and azimuth changes).

There are a number of features that are useful when selecting a planning tool such as

Automatic frequency selection
Optimal site selection—when existing or candidate sites are provided
Support of mixed and multiple propagation models
Support of model tuning and user defined models
Support of OFDMA system including channel impairments
Optimal downtilting
Propagation parameters (or constants) for 2.5 and 3.5 GHz

A number of commercial planning tools are available in the market. The major factor that determines the usability of the tool is the accuracy of the RF modeling such as propagation, BTS and CPE antenna models, interference prediction, frequency allocation, and channel models. Planning tools with OFDMA models for capacity planning are advantageous but not necessary since the capacity figures for each site of cluster can be estimated based on the signal quality outputs.

INTEGER PROGRAMMING MODEL | Network Planning for IEEE 802.16j Relay Networks

2011-11-25T07:11:00.000-08:00

Four sets of tests were performed with the basic variant of the problem to determine its sensitivity to different parameters.

In the first experiment, all three parameters were scaled—the number of candidate BSs, candidate RSs, and TPs. The number of BSs was varied and the numbers of RSs and TPs were three times and ten times this figure, respectively. Figure 1 shows how the time required finding a solution scales up. As it can be seen, the problem can be solved for up to 80 candidate BSs and 240 RSs with ease. Further, the results show that the problem complexity is scaling up quite rapidly. Indeed, further experiments were performed in which the number of candidate BSs was increased to 120 and the resulting execution mean time was under 30 min. The system is exhibiting scaling properties which are quite nonlinear, although some basic curve fitting has shown that for the available data set, the scaling is considerably less than exponential.

Figure 1: Calculation time when three parameters are scaled at the same time.

Figure 2 shows the calculation time when only the number of BSs is scaling. The number of RSs is set to 90 and the number of TPs is set to 300 in all tests.

Figure 2: Calculation time when only the number of BS is scaled.

A similar experiment was performed in which the number of RSs was scaled up and the number of BSs and TPs remained constant. Again it is clear that the system is scaling up linearly in this parameter (Figure 3). The number of BSs is set to 30 and the number of TPs is set 300 in all tests.

Figure 3: Calculation time when only the number of RS is scaled.

Finally, in this set of experiments, the sensitivity to the number of TPs was considered. The same characteristic is again observed: the system scales linearly as can be seen from Figure 4. The number of BSs is set to 30 and the number of RSs is set to 90 in all tests.

Figure 4 Calculation time when only the number of TP is scaled.

From the figures, it can be seen that this algorithm should suit small size network planning problems since the time cost is very short for small number of BSs. The time varies almost linearly if individual parameter is varying. For the problem sizes studied—which are typical for small metropolitan scenarios—the solution can be found quickly on typical desktop computers, e.g., under two minutes for problems with 50 candidate BS sites, and approximately ten minutes for problems with 100 candidate BS sites. The time cost for the planning could increase to one day long or a few days to plan a larger network, e.g., around 500 candidate sites, but it is still practicable.

RESULTS AND DISCUSSION | IEEE 802.16j Relay Networks

2011-11-14T06:29:00.000-08:00

The objective of these tests can be divided into two parts. One is to obtain an understanding of the scalability of the problem formulation—the basic and the state space reduction model. More specifically, the objective was to understand if this problem formulation can be used to solve problems of realistic size. Given that it is, in principle, an NP-hard problem, it is important to understand the range of problems for which standard solution techniques are appropriate and the range of problems which require the development of heuristics which employ domain knowledge.

The second is to determine how the clustering approach compares with the more rudimentary approaches. The comparison was performed based on both the time taken to obtain a solution and the quality of the resulting solution; naturally, the former relates directly to the scalability characteristics of the approach and its applicability for realistic scenarios.

A number of tests were performed in which the number of BSs, RSs, and TPs were varied. All tests were done using a standard desktop computer—Centrino Duo 2.0 GHz, 1 GB Memory, Windows Vista. Twelve tests were performed each time and the mean execution time taken. As there was some variation in the results, the minimum and maximum execution times were removed and the mean taken over the remaining ten results.

Problems were generated at random. The locations of each of the BSs, RSs, and TPs were chosen randomly from an area of size 3 × 3 km. The (x, y) coordinates of each node were chosen by selecting two random variable from the distribution U(0, 3000). For each of the problems the same set of weight parameters were used: λ₁ = 8, λ₂ = 8, and λ₃ = 20. However, it is worth noting that the values of these parameters have little impact on the time required to find solutions. In each of the problems, the BS cost was chosen at random and was three times the cost of the RS.

In all of the following tests, the branch and bound method found the optimal solution to the given problem. Figure 1 shows one possible result for planning a network with 20 candidate BSs, 60 candidate RSs, and 200 TPs. In the solution, 10 BSs are selected with 36 RSs

Figure 1: A typical output of the planning tool.

RELAY STATION CAPABILITIES | IEEE 802.16J

2011-11-11T02:20:00.000-08:00

As the standard is still evolving, it is not clear what the final variant will look like. However, at present, it appears that two categories of RS will be defined: low capability RS (simple RS) and high capability RS (full function RS). The simple RS is used for low cost deployment, and operates on one OFDMA channel. It contains no control functionality (i.e., control functions are centralized in the MMR-BS) with one transceiver and optionally supports multiple input multiple output (MIMO). The full function RS can operate on multiple OFDMA channels, implement distributed control functions, and support MIMO. This type of RS has a further two variants: fixed/nomadic full function RS and mobile full function RS. Mobile RSs add support for handover and the ability to deal with a varying channel due to mobility. Table 1 summarizes the different RSs capabilities.

Table 1: RS Capabilities
	Simple RS	Full Function Fixed/Nomadic RS	Mobile RS
Number of OFDMA channels	1	≥1	≥1
Duplexing on MMR and access links	TDD	TDD or FDD	TDD or FDD
Frequency sharing between access and MMR links	Yes	Yes or No	Yes or No
Mobility	Centralized in MMR-BS	Centralized in MMR-BS or distributed in RSs	Centralized in MMR-BS or distributed in RSs
Antenna support	SISO or MIMO	MIMO	MIMO

At present, it is considered that an MMR network could be composed of multiple usage models including multiple RS types specifically deployed. But at present, there is only a little work about the heterogeneous functionalities of the RSs in different scenarios.

For example, an MS can move from the coverage provided inside a building by fixed/nomadic RS to a train where the coverage is provided by a mobile RS. Furthermore, there is no direct mapping between the usage models and the types of RS. An operator may deploy a variety of different RS types depending on traffic, mobility, topology (two hops or more) within the area of each RS location for a specific usage model.

In fact, the future standard will not answer all the issues raised by the RS incorporation to provide vendor differentiation. For instance, intelligent scheduling either at the BS (in a centralized approach) or at the BS and RSs (in a distributed approach) are required to minimize the interference that occurs at the RSs.

OVERVIEW OF IEEE 802.16J

2011-11-07T03:33:00.000-08:00

In IEEE 802.16j low cost RSs are introduced to provide enhanced coverage and capacity. Using such stations, an operator could deploy a network with wide coverage at a lower cost than using only (more) expensive BSs to provide good coverage, and increasing significantly the system throughput. As network utilization increases, these RSs could be replaced by BSs as required. The mesh architecture defined in WiMAX is already used to increase the coverage and the throughput of the system. However, this mode is not compatible with the point-to-multipoint (PMP) mode with no support of the OFDMA PHY, fast route change for mobile station (MS), etc. Hence, the standards organization has recognized this as an important area of development, and today a task group is charged with drafting a new standard: the IEEE 802.16j mobile multihop relay design to address these issues. The first draft of the IEEE 802.16j standard has just finished in August 2007.

IEEE 802.16J SCOPE

The IEEE 802.16j is aiming to develop a relay mode based on IEEE 802.16e by introducing RSs depending on the usage model:

Coverage extension
Capacity enhancement

In other words, the relay technology is first expected to improve the coverage reliability in geographic areas that are severely shadowed from the BS or to extend the range of a BS. In both cases, the RS enhances coverage by transmitting from an advantageous location closer to a disadvantaged SS than the BS. Second, it is expected to improve the throughput for users at the edges of an 802.16 cell. It has been recognized in previous 802.16 contributions that subscribers at the edges of a cell may be required to communicate at reduced rates. This is because received signal strength is lower at the cell edge. Finally, it is expected to increase system capacity by deploying RSs in a manner that enables more aggressive frequency reuse. Figure 1 illustrates the different scenarios in which relay mode could be used. However, introducing such RSs considerably alters the architecture of the network and raises many issues and questions. It is still unclear what system design is appropriate and can be realized at a low cost while still providing good coverage with an enhancement of the throughput.

Figure 1: IEEE 802.16j example use cases.

The 802.16j task group’s scope is to specify OFDMA PHY and MAC enhancement to the IEEE 802.16 standards for licensed bands. These specifications aim to enable the operation of fixed, nomadic, and mobile RSs by keeping the backward compatibility with SS/MS. In other words, the standard will define a new RS entity and modify the BS to support Mobile Multihop Relay (MMR) links and aggregation of traffic from multiple sources. An MMR link represents a radio link between an MMR-BS and an RS or between a pair of RSs. Such link can support fixed, portable, and mobile RSs and multihop communications between a BS and RSs on the path. An access link is a radio link that originates or terminates at an SS/MS. Table 1 illustrates the main scope of the project.

Table 1: IEEE 802.16j Project Scope
		Define New
No Change	Changes to BS	RS Entity	“802.16j Relay” Link Air Interface
To SS/MS To 802.16e OFDMA PMP link	Add support for MMR links Add support for aggregation of traffic from multiple RSs	Supports PMP links Supports MMR links Supports aggregation of traffic from multiple RSs	Support fixed, portable, and mobile RSs Based on OFDMA PHY MAC to support multi-hop communication Security and management

TECHNOECONOMICS OF DIMENSIONING

2011-11-03T04:50:00.000-07:00

As already stated several times, the business plan and the dimensioning strategy are the main factors that affect the network size and overall investment. On top of the access network, a backhaul network should be implemented to connect the access with the core network, and there is also the core network infrastructure. The overall equipment depends on the number of PoP, almost in a linear manner. Further to the equipment costs, the deployment engineering costs should be also taken into consideration.

COST INCREASING FACTORS DURING DIMENSIONING

The dimensioning output may lead to an oversized/undersized network mainly for two reasons: either due to the business plan or due to a low quality study. In the first case it is the responsibility of the author of the business plan if it is not so realistic, while in the second case it is the responsibity of the designer if a study outcome is of low accuracy. The main parameters that impact the network size and hence the costs can be seen in Table 1. An ambitious business plan will most likely lead to a significant investment. However, in terms of network design and implementation, a huge network increases design complexity which results in longer time-to-market. A scalable deployment is preferable since a higher design quality can be obtained. In contrast, lack of scalability would further extend the transition period until the network implementation is finalized. The costs and complexity also depend on the number of PoP. If the distance between PoP is very small (i.e., less than 0.5 km) then the design complexity is increased significantly. For capacity-limited networks that require too many sectors it would be cost effective to use higher sectorization per BS or a dual sector layer if possible. Finally it should be mentioned that for areas under heavy construction the expected terrain changes should be considered since they may result in a long design and implementation period. Any condition that would result in longer engineering times (design, implementation) would also further increase costs. Furthermore, terrain changes may alter the coverage and hence a revision may be necessary after a period.

Table 1: Dimensioning Size and Cost Increasing Factors
Factor	CapEX/OpEX	Complexity	Time-to-Market
Ambitious business plan	Very high	Very high	Very long
Unbalanced number of PoP	High	High	Long
Luck of scalability strategy	High	Very high	Long transition
Environmental changes	High	High	Long transition

SCALABILITY | STRATEGY INTERACTION

2011-10-30T09:29:00.000-07:00

As mentioned previously, the most significant advantage of WiMAX is the flexibility and scalability of the air-interface. Therefore the concept “pay as you grow” is definitely applicable in WiMAX commercial networks. Scalability allows the reduction of initial investment and risks, and thereafter the network is expanded based on the market penetration and revenues. The majority of business plans provide the subscriber numbers and services per deployment year and in this context the dimensioning should be presented according to the scalability plan (network size/performance per year). The complexity in this case is not the increase of subscriber numbers or service area size, but when in parallel the coverage type is upgraded or new products (based on recently released standards) have to coexist with deployed equipment. The main types of scalability plan that have to be considered during dimensioning are presented as follows:

Network expansion: Expansion may be in terms of subscriber numbers, service rates, or service areas or a combination and involves the deployment of new PoP or addition of sectors in existing PoP. A great challenge, in such scenario, is to optimize the positioning of PoP to achieve the best coverage and capacity outcome. It is more appropriate to determine the network size and PoP positions for the last deployment year, and then deploy only a subset of PoP that would satisfy the objectives of the initial phase.
Coverage upgrade: It is common to allow the use of more demanding terminal profiles (i.e., nomadic/mobile) in the network after the first year so as to allow time to test the performance of the wireless network. In this case, not only the network is expanded from first to second year, but also the coverage should be upgraded too. The same approach “design for the future, deploy for present” as above should be applied, and the only difference is that a more dense network is probably required.
Technology upgrade: The major change in terms of equipment is between the IEEE 801.16-2004 and 802.16e standards. The new products are based on software defined radio technology and therefore future standard releases will probably be implemented with minor changes. It is quite challenging to upgrade an existing fixed WiMAX network to coexist with mobile WiMAX, unless there is provision for additional spectrum. The major challenge is to replace subscriber equipment and restore access and it is likely that this transition phase will take a long time.

CUSTOMIZED NETWORK DESIGN | WiMAX Networks Dimensioning

2011-10-27T08:28:00.000-07:00

Customized network dimensioning provides a major advantage to vendors/integrators by highlighting their expertise and by indicating a cost-optimum solution. In many RFPs or project cases the developed business plan has extensive details which can be exploited in a very positive manner. A common occasion is that the overall service area is broken down into subareas with distinct characteristics, such as common type of customers and terminal profiles, common terrain, or service requirements. Such distinction allows a customized treatment of each subarea, where its characteristics are matched with an optimum solution, thus avoiding an overall rough approximation. The main customizations that can be applied in dimensioning, provided that the necessary information is available, are described below:

Nonuniform sectorization: The use of different sectorization schemes, depending on the coverage type may result in reducing the required PoP. In some subareas there might be only SMEs, where a fixed-outdoor unit would be utilized. Therefore, in this case there is no restriction to use trisector cells. If the available spectrum is sufficient, up to eight sectors can be deployed in a PoP, depending on the capacity requirements.
Dual layer coverage: In many occasions the subscribers will use both fixed-outdoor and mobile units hence in this case a more efficient approach is necessary. If extreme capacity is required, a high number of sectors can be used, building a trisector layer for the mobiles and overlapping layer for the fixed, provided that frequency reuse can be applied. The neighboring list for mobile handovers will include only a sector in the first layer, while the demanding fixed links will be isolated in the second layer. This approach is very cost effective in cases where an extreme capacity demand would require a PoP every 0.2–0.5 km, if plain trisector cell layout was considered.
Nonuniform channel bandwidth: Assuming that extreme capacity is still the performance indicator, by using higher channel bandwidth the capacity per PoP is increased and therefore the number of PoP is maintained at a reasonable level. While the overall network may be implemented with trisector cells, 5 MHz channel bandwidth and 1.3.3 reuse scheme (using 3 out of 4 available channels), a specific subarea with SMEs may be served by quad-sector cells, with 10 MHz bandwidth. In this case, the 10 MHz in the upper band will be assigned in sectors 1 and 3, while the 10 MHz in the lower band in sectors 2 and 4. This arrangement provides a 260 percent increase in capacity for the same footprint. It should be noted that the use of nonuniform channel bandwidth requires specialized sector configuration (transmit power, UL receive target level, and antenna arrangement) and extensive interference studies, applying the interference rejection filter methodology that takes into account the impact from trasmissions in all channels.
Hybrid coverage: When both fixed-outdoor and nomadic/mobile coverage is required, the design approach is to select the operating range for the worst case condition. This usually leads to a huge upfront network size and investment. An alternative approach would be to consider that only a portion of the service area will be covered with the worst case terminal, hence the cell range can be selected higher. As the network evolves and additional PoP will be deployed for capacity upgrade, the percentage of the mobile coverage in the service area will also increase. This approach is particularly efficient for networks that extend to a large area, however with low subscriber density. The dimensioning is based on fixed-outdoor coverage that can reach higher ranges, however close to a BS there will be opportunity to use fixed-indoor/nomadic/mobile terminals also.
Divide and conquer principle: To apply the previous customizations, the service area can be processed in pieces, where each subarea has a specific characteristic. If the segmentation of the service area is not available in the business plan, the designers can perform such action, by requesting or collecting more information (i.e., from site survey). This principle usually drives the network size up, and it is most useful for the actual design phase. The equipment quantities are increased in an exact manner because greater detail is taken into account during the actual design. This method approaches an optimum wireless network design.

It should be mentioned that the advances of the WiMAX air-interface allows greater flexibility and customizations during the wireless network design and hence balances the complexity of accommodating terminals with different profiles.

PRESENT–FUTURE TECHNOLOGIES

2011-10-23T02:00:00.000-07:00

Over the last two years IEEE 802.16-2004 products have become more widely used and with several deployments in the field, basic experience with WiMAX technology has been increased. The new, upcoming IEEE 802.16e, which is an amendment to the IEEE 802.16-2004 standard, promises significant improvements. The major enhancements in WiMAX technology for the upcoming version of the standard are outlined in Table 1.

Table 1: Evolution of IEEE 802.16 Technology
Technology	IEEE 802.16d	IEEE 802.16e	IEEE 802.16j
Range (km)	0.7	1.8	2 (per hop)
AAS	–	STC/MRC/SM/BF	Cooperation
Air-interface	OFDM	OFDMA	Relay-based
QoS	Basic	E-rtPS	Enhanced
Profile	IP Padios	ASN	ASN
Scenarios	Nomadic	Mobile	Mobile relaying

The most important enhancement concerns the system gain, which is roughly increased by 15–25 dB, and hence the cell range is also increased. For fixed/nomadic terminals it is evident that 802.16e is much more efficient and is also capable of catering for mobile terminals. Another decisive improvement is the introduction of AAS, which increases robustness through STC, MRC, and spectral efficiency through spatial multiplexing (SM) and BF. The transition to OFDMA clearly boosts the radio resource management efficiency and improves QoS particularly in the presence of VoIP service. It is evident that the evolution of WiMAX targets three objectives: to increase the system gain and reach customers inside their homes/offices and on the move, to boost capacity so as to reduce service costs and be competitive with other access technologies, and finally to coexist in a seamless manner in the upcoming all IP networks.

This is clearly the case with the development IEEE 802.16j which introduces the concept of relays. A relay-based network can in principle extend the range boundlessly, however, in practice 2-hop links are more likely to be implemented (for delay and throughput issues). From the designer’s perspective if the first link (BS-relay) is line of sight then the system range can be several times higher than conventional systems, and hence for coverage-limited networks the dimensioning would result in much reduced costs. Furthermore, if the BS employs BF toward the relays, concurrent communication with several of them may be established in the form of spatial-division multiple access (SDMA), therefore boosting cell capacity.

While the all IP architecture is on the way (access service network (ASN) gateway), with major manufacturers of network products supporting this direction, the next WiMAX standard, IEEE 802.16m is also under consideration. IEEE 802.16m will revise the air-interface in the scope of international telecommunications union–radiocommunication sector (ITU-R) requirements for IMT-2000 and IMT-Advanced.

DIMENSIONING CERTAINTY AND MARGINS

2011-10-09T03:27:00.000-07:00

As stated above, dimensioning accuracy is crucial. For vendors/integrators, it is essential that an RFP response should be financially prudent to increase chances for a contract award. However after the award, the network should be deployable, and considering that the majority of RFPs concern turn-key projects, a rough or underestimated offer can lead to miscalculation of the required network size and hence increase the implementation costs at the vendor’s/integrator’s expense. A balanced condition can be achieved by incorporating operating/performance and certainty margins. An operating margin is applied to coverage estimations, where the system range is selected smaller than the maximum. The same applies to capacity estimations where, for the average sector throughput, usually a small margin is considered. Considering the overlapping effect of various margins, a careless consideration can lead to overestimation. Specifically for coverage and capacity margins, the network will be either coverage-limited or capacity-limited. Therefore, in practice only one of the previously mentioned margins has impact on the financial offer. An important factor for defining margins is the quality of the provided business plan. The provision of extensive information would facilitate more accurate dimensioning.

It should be noted that in addition to the operating margins, another issue to consider is the network implementation margins. Although the RF designers take great caution to predict any possible causes of degradation, in many occasions problems may occur during the implementation. An engineering team, which is not well trained, or pays little attention to details, can make the difference between a successful and poor deployment. In cases where existing infrastructure is utilized, such as sharing of GSM sites, the condition of these sites or the restrictions posed by an operator usually cause problems. For example to save cost of antenna poles, operators may install WiMAX antennas below GSM antennas. This is contradictory to best practice since WiMAX operates in higher band and hence experiences higher propagation losses, and furthermore there is the issue of equipment RF isolation where a minimum separation distance should be maintained. Another example is with the installation of fixed-outdoor CPEs. Careless installation will result in suboptimum performance of such units, compromising their competitive advantage which is high capacity and robustness. In general, past deployment experience can make the difference in dimensioning, and it is preferred that the RF network designers which are involved in presales activities are the same that will have the responsibility of carrying out the final design and deployment supervision.

EQUIPMENT SPECIFICATIONS

2011-10-05T00:15:00.000-07:00

From a WiMAX access network design perspective, the most important parameters are related to the PHY and MAC characteristics of the air-interface. Typically all parameters that affect the link gain budget, such as transmit power, antenna gains, receiver sensitivities, advanced antenna systems are of great importance. Consider an example of a WiMAX access network that is intended to serve stationary indoor terminals in a typical suburban environment (i.e., SUI-C channel), hence a coverage-limited scenario. Comparing two systems with MIMO 2x and BF 8x configuration,the system range is around 2.2 and 3.7 km, respectively. This result is for the same amplifier output where the 6 dB system gain is due to the difference in antenna elements. Note that the diversity or BF gains are not considered for the range estimations since they are only applied in user traffic and not in the signaling part of the frame that usually limits the range. Applying Equation 13.5, the footprint is estimated as 12.5 and 35.5 km², respectively. Results indicate that for coverage limited scenarios the higher system gain of a BF configuration can significantly reduce the network size. On the other hand, for mobility scenarios the use of MIMO is a more suitable approach. In general WiMAX has several air-interface profiles that may be best suited according to the deployment scenario.

In addition to the link gain budget, the sector capacity is equally significant for capacity-limited scenarios. A comparison of the spectral efficiency per modulation and the required SINR thresholds could indicate that some products may operate with higher efficiency than others. Capacity is further increased by advanced antenna systems, where spatial multiplexing could even double the spectral efficiency, while BF could reduce interference levels and upgrade the PHY mode.

A list of important parameters, with impact on coverage, capacity, and QoS are shown in Figure 1. These parameters should be provided for all combinations of operating frequency band-channel bandwidth, both for the DL and UL and for different terminal profiles. A vital parameter for dimensioning is the number of subscribers that can be supported in a sector due to availability of service flows. This number depends on the number of service types per user. Additionally, a description of the capabilities and performance of the radio resource management (scheduler) would be beneficial to the designers. It is common during dimensioning to assume that the system can preserve QoS but in many occasions a safety margin (i.e., 5 percent) is required. The scheduler’s performance may downgrade close to full capacity load or for high number of subscribers per sector. Considering BE traffic, the impact is not as significant, however, this is not the case for VoIP and other delay-sensitive services. When handover is involved, it becomes more challenging to preserve QoS. It is evident from this section that WiMAX networks designers should have a thorough under-standing of technology so as to foresee potential issues during network dimensioning and hence consider appropriate margins. A good insight into WiMAX air-interface performance issues.

Figure 1: Important WiMAX air-interface parameters.

BACKHAUL DIMENSIONING INPUT

2011-09-24T04:24:00.000-07:00

One result of the dimensioning of the access network is the required capacity by the backhaul network. A typical backhaul network consists of point-to-point or point-to-multipoint radio links or even fiber rings in recent networks. In most cases the information rate that is originated to and from the backhaul network should be calculated to evaluate an existing infrastructure or consider the cost to deploy a backhaul network along with WiMAX.

The total committed information rate of a WiMAX PoP is linear to the number of sectors and average sector throughput, which mainly depends on channel bandwidth and deployment scenario. Considering trisector cells, and average throughput of 9 Mbps (5 MHz channel bandwidth), the total TDD committed rate is 27 Mbps. To estimate the FDD equivalent, which is directed to and from the backhaul network, the total rate is multiplied by the DL/(DL + UL) ratio. Hence the traffic that should be reserved in the backhaul network for a PoP with 2/3 DL WiMAX traffic is 18 Mbps. For a dual layer cell or 10 MHz channel bandwidth the backhaul rate per PoP would be doubled, around 36 Mbps. It is acceptable to include a 5 percent margin as in practice it is common to observe small deviations in sector throughput. During the deployment and the operation the exact backhaul traffic can be monitored through the management system and design adjustments can be made accordingly.

In certain occasions the network designers are requested to propose a backhaul system along with the WiMAX network. Although this is not in the scope of this chapter, however there are some interesting observations that can be highlighted. Clearly the multipoint systems are more cost effective than point-to-point radio systems when the WiMAX PoP number is high, the PoP are quite close to each other and when their backhaul rate is such that more than 3–4 WiMAX PoP per multipoint sector can be served (around 10–12 per site). This indicates that the WiMAX site backhaul traffic should be from 15 to 25 Mbps, which mainly refers to trisector cells with 5 MHz channel. For higher bandwidth systems, such as dual layer cells or with 10 MHz bandwidth per site the point-to-point solution may offer the higher required capacity. Again in a dense urban environment where sites are usually deployed with separation of 0.3–0.7 km the use of fiber (if available) could be a better solution. Concluding, the best backhaul system strategy can be determined by evaluating the number of WiMAX PoP, their backhaul rate, and finally the PoP positioning. Note that although the WiMAX network dimensioning is done in such way to optimize the access network, this does not suggest that the backhaul network is also optimized and a separate study may be necessary.

JOINT DIMENSIONING

2011-09-21T01:31:00.000-07:00

The number of PoP and sectors were estimated according to the requirements of a dimensioning project. The final step is to combine these results into the optimum BS configuration. Clearly the estimated PoP and sectors are the absolute minimum according the needs of coverage and capacity, respectively. At this stage joint consideration may suggest that more PoP or sectors may be necessary. There are three possible conditions:

Balanced network: The number of PoP approaches 1/x of the number of sectors, which means that in each PoP roughly x sectors will be deployed. The number of sectors for blanket coverage should be 3 < x < 6, where x = 3 for Mobile WiMAX. This condition ensures both the integrity of the footprint and satisfies the capacity requirement.
Coverage-limited network: The number of PoP is quite higher than 1/3 of sectors. The network is coverage limited and in this case the number of sectors should be increased until the previous condition is met. The fact that the original business plan leads to a coverage-limited network should be stated in the dimensioning study. CapEX is driven by coverage performance indicators, while the additional sectors will further increase the air-interface capacity. Operators may want to revise the size of the service area, or exploit the additional capacity.
Capacity-limited network: The number of PoP is quite lower than 1/3 of sectors, which indicates either additional PoP or higher sectorization (sectors/PoP). Increasing the number of PoP will trigger additional CapEX and OpEX in terms of site acquisition and preparation. Therefore, if a higher sectorization scheme is possible, such as when the terminals are fixed-outdoor, fixed-indoor, or nomadic where handover is not necessary, it should be preferred as a cost-optimum solution. When the network needs to accommodate mobile terminals and provide handover capability, the sectors should be 3 < x < 4 and more PoP may be needed. An alternative approach would be to deploy a dual layer cell where 6 sectors of 120° are used, however each pair of sectors (i.e., 1 and 4) is assigned the same azimuth. For a dual layer cell at least 6 channels are necessary for the frequency reuse of 1.3.3.

The selected number of sectors per PoP defines the BS configuration in terms of frequency reuse/channel assignment, and antenna beamwidth/azimuth/tilt, while other air-interface parameters are not related to dimensioning. Capacity or coverage dimensioning should be revised based on the above-mentioned conditions, for coverage or capacity limited cases, respectively. A comparison between initial requirement and actual achievement should be included in the dimensioning study.

CAPACITY DIMENSIONING - 4G

2011-09-18T08:00:00.000-07:00

Further to providing adequate radio coverage to customers, the next equally important objective is to ensure sufficient air-interface capacity (throughput) to offer a wide range of services. Given a business plan, the network capacity is driven by the potential subscriber number and the committed rates for data and VoIP services, as discussed in services dimensioning. The purpose of capacity dimensioning is to translate the capacity into number of sectors, which then have to be distributed for the estimated number of PoP. The missing parameter is average operating sector throughput, which finally determines how many subscribers can be served in a sector. Defining the average sector throughput is quite complex as it involves interpretation of a wide range of parameters such as

Accurate equipment specifications: PHY modes and SINR thresholds, advanced antenna system impact, interference mitigation mechanisms, scheduling intelligence, and terminal types.
Deployment considerations: Available spectrum, reuse factor and channel assignment method, coverage type(s), propagation environment, and site suitability.
Designer capabilities: Very good knowledge of the IEEE 802.16e standard, formal training or academic expertise in planning methodologies and their impact on practical networks, past deployment experience.

The sector throughput is usually provided as recommendation by the system vendor, however it can vary a lot depending on the deployment scenario. During dimensioning a more accurate estimation can be made through two approaches:

(1) By extensive RF planning simulations and statistical processing that would result in a PHY mode regions map and give the average throughput. If clutter data are available this approach may provide a better view of the network performance.

(2) By previous experience and interpretation of the above factors based on system performance observation. The former approach is time consuming, however it is much more accurate and is usually followed during actual network design. The latter is more appropriate during dimensioning.

Typically the equipment specifications include a table that matches the available PHY modes with their SINR requirement and the resulting Ethernet throughput (for different channel bandwidths) as in Table 1. The SINR values are given in the standard. The provided Ethernet rates correspond to a 60/40 DL/UL asymmetry and hence the TDD rate is the sum of DL and UL rates. The upper throughput bound can be achieved for an interference-free sector where terminals are located only in the 64QAM, 3/4 region, achieving around 10 Mbps TDD for the 5 MHz bandwidth. During practical deployments, the terminals will be scattered across the whole cell footprint, hence operating in various modes. Furthermore, interference due to frequency reuse may further downgrade the PHY mode for a particular terminal, especially if the number of channels is limited. A default assumption is to consider, as average sector throughput, the one corresponding to 16QAM, 1/2. Thereafter if the deployment conditions are favorable, as in the case of fixed-outdoor terminals or when enough spectrum is available for relaxed reuse, higher throughput should be expected. The throughput can be also enhanced by means of MIMO techniques and this should also be taken into account. It should be noted that the values in Table 2 refer to full usage of subcarrier (FUSC) permutation scheme, where all subchannels are allocated to users, hence the whole channel is exploited. In case segmentation is considered, i.e., partial usage of subcarrier (PUSC), the users in a sector utilize a specific segment (1–6 subchannels), and therefore the throughput in this case is reduced accordingly. Based on the standard, there will be regions in the DL and UL subframes for both FUSC and PUSC and in this case an average throughput condition should be expected. In most products, during the sector configuration, an RF designer can select or exclude segmentation according to deployment conditions.

Table 1: Ethernet Rates per PHY Mode (IEEE 802.16e System)
		5 MHz Bandwidth		10 MHz Bandwidth
PHY Mode (CTC)	SINR (dB)	DL (Mbps)	UL( Mbps)	DL (Mbps)	UL (Mbps)
QPSK 1/2	2.9	1.4	0.9	2.7	1.7
QPSK 3/4	6.3	2.2	1.3	4.3	2.7
16QAM 1/2	8.6	2.8	1.7	5.8	3.6
16QAM 3/4	12.7	4.3	2.6	8.6	5.4
64QAM 1/2	16.9	5.8	3.5	11.6	7.1
64QAM 3/4	18.0	6.5	3.9	12.9	8.2

The process of selecting frequency reuse and channel allocation in sectors is very important for both capacity and coverage. In mobile WiMAX this can be done in a flexible manner, although frequency planning cannot be avoided. This is due to the possibility of nonuniform network layout, in most cases, where frequency planning may improve performance. According to mobile WiMAX terminology the reuse is denoted as 1.x.y, where x denotes the cell sectors and y the available channels. There are two main schemes under consideration: global reuse 1.x.1, where a single channel is used everywhere and cell/cluster reuse 1.x.y, where y = nx, n = 1, 2, 3. The most appropriate scheme is adopted, based on the systems’s special capabilities to reject or tolerate interference (i.e., via BF). An indicative performance of the most common schemes for nomadic/mobile terminals (most sensitive to interference) is presented in Figure 1. It can be observed that for the 1.3.1 scheme the SINR drops well below the 3 dB threshold for the lowest modulation scheme, QPSK, hence a significant part of the cell footprint, especially among adjacent sectors, has no coverage. In the case of the 1.3.3 scheme, the interference appears closer to the cell edges and hence the coverage blanks spots are much smaller. It should be noted that in the 1.3.3 scheme the higher order PHY mode schemes extend to a larger region, hence indicating an improved sector throughput. Furthermore, when employing PUSC instead of FUSC, the 1.3.1 scheme behaves essentially as 1.3.3, while 1.3.3 as 1.3.9. It is typical for a sector to operate in 1.3.1 FUSC mode for terminals with good link quality and short link distance and in PUSC mode, which is equivalent to 1.3.3 for terminals that would otherwise achieve low SINR (due to low signal strength or interference).

Figure 1: SINR map for (FUSC) and (PUSC) schemes.

Knowing the average sector throughput as described in previous paragraphs, capacity dimensioning can be completed as follows: Initially an analysis on the customers that can be accommodated in a sector is performed. This is done by analyzing the service plan and calculating the average data and VoIP CIR per service and customer. Then a graph that shows the throughput versus the subscribers is obtained, as in Figure 2. The average sector throughput is projected in the sector total graph indicating the corresponding subscriber number. Considering a 10 Mbps throughput, each sector can accommodate around 70 customers. Moreover a graph such as in Figure 2 is quite useful also during network operation as when the throughput takes a higher value, due to special circumstances, additional subscribers can be served. The second step is to divide the number of customers per area with the customers/sector to calculate the required number of sectors.

Figure 2: Estimated sector and services throughput versus subscribers.

DIMENSIONING PROCESS

2011-09-14T07:57:00.000-07:00

The process of dimensioning involves a sequence of steps, which serve different requirements such as capacity or coverage estimations, to conclude to a final outcome as shown in Figure 1. The input consists of the business plan, the assets, and the key performance indicators (KPIs). The first action is then to verify that all necessary information is available and clarified, alternatively an interactive “questions and answers” session is necessary. During the input analysis, the designers utilize their theoretical and technological expertise to define the dimensioning strategy. In somecases this analysis identifies that potential business plan assumptions could affect the network design process, and a discussion is likely to be initiated with the customer. Such an approach is more appropriate after the award of a contract. The following step is the processing of service characteristics such as the Internet rates and Voice-over-Internet Protocol (VoIP) codecs. In most cases the services are provided in the business plan with their marketing description and it is necessary to identify the impact of each service on the WiMAX air-interface. In the next step, the coverage analysis is performed: the provided service areas are identified and the required number of points of presence (PoP) is estimated. A PoP refers to a WiMAX site, with at least three sectors, which is capable of providing a 360° footprint.

Figure 1: Network dimensioning process.

Further to the coverage analysis, the service areas should be also offered sufficient capacity as dictated by customer numbers and services profiles. Therefore the next step is to estimate the required number of sectors to achieve this capacity. The estimates of PoP and sectors are then used to determine the configuration of the BS in the final joint analysis step. It is common after the joint analysis for designers to evaluate the results and proceed with customizations to further improve the solution. In this case, coverage, capacity, and joint analysis may be revised several times before providing the final output. A description of the strategy, the final bill of material (BoM), and a discussion of the assumptions and methodology are mainly the outcome of a dimensioning study.

1 BUSINESS PLAN, ASSETS, AND KEY PERFORMANCE INDICATORS

A well-defined business plan can be the most significant driver behind a successful investment on WiMAX technology. From a cost point of view, a very ambitious plan in terms of subscribers/services will certainly impact CapEX and OpEX, whereas from a design point of view, the impact comes when the requirements are not extensively defined or they contradict the technology capabilities. A revision of the business plan during or after the network design may compromise the network performance or create additional design costs but most importantly it will result in implementation delays. A list with the most common information provided with a business plan is presented as follows:

Service area(s): defined with geocoded polygons, including the size in km², and the terrain profile details (i.e., urban, suburban, rural, average building height, etc.).
Coverage type: such as fixed-outdoor, on rooftop, or on outer walls, fixed-indoor, nomadic outdoor/indoor, mobile outdoor or any combination thereof.
Subscriber profile(s): such as residential, small business, corporate. Subscriber profiles may relate to a specific type of coverage and service.
Subscriber distribution: subscriber numbers per profile, per service area, and per deployment year, according to the scalability plan.
Service profile(s): such as VoIP, broadband Internet, VPN along with their distinct characteristics (i.e., VoIP codecs, peak information rates, contention factors, etc.). Service profiles may relate to specific subscriber profiles and coverage types.
Available spectrum: defined as paired, along with local regulations concerning the allowed channelization and duplex schemes.
Existing infrastructure: such as sites that can be reused, available backhauling equipment with Ethernet interface, and core network PoPs.
Cartographic data: such as high-resolution digital maps with buildings.
Key performance indicators: such as coverage objective in terms of percentage of the service area, differentiated per terminal type, where a stable QPSK link can be achieved.
Customer requirement: such as duplex scheme, number of sectors/BS, channel bandwidth, reuse scheme, type of sites, deployment strategy.

2 STRATEGY

During request for information (RFI)/RFP stages, a dimensioning exercise may be requested by a customer, mainly for two reasons: either to acquire know-how by differentiated proposals or to identify the more cost-efficient solution. In the first case, the requirements are usually relaxed so that the participant vendors/integrators can design with flexibility, while the provided information (i.e., business plan, assets, service areas) is hypothetical. The submitted studies will probably be presented in various formats and most certainly based on diverse assumptions. In such case a direct comparison among the studies is complicated, and usually a more defined exercise is the next step. In the second approach, the case study is well defined so that the design assumptions are either implied or directly mentioned. The results are now directly comparable, hence a clear ranking list can be obtained. From RF network designer point of view a different strategy should be followed: showing flexibility in the network design and perhaps providing several alternatives for the first approach, while a more strict, cost-optimum solution is more appropriate for the second approach.

DEPLOYMENT SCENARIOS

2011-09-10T08:10:00.000-07:00

A major feature of WiMAX compared to other wireless access technologies is that it breaks the barrier of addressing a single customer profile. Global system for mobile communications (GSM)/universal mobile telecommunications system (UMTS) provide mainly voice and low speed internet to mobile subscribers, while local multipoint distribution service (LMDS)/wireless local loop (WLL) offer higher bandwidth services to fixed subscribers. WiMAX can offer broadband services to all fixed, nomadic, and eventually mobile subscribers, according to the aims of the latest IEEE 802.16e standard. This major advantage for WiMAX technology offers greater flexibility and scalability; however it presents more design challenges. A conceptual presentation of deployment scenarios, based on equipment, services, and potential customer profiles is presented in Figure 1. Each “sector” represents a WiMAX terminal profile:

Fixed-outdoor units (including antenna, RF subsystem, modem), which can be installed on the rooftop or outer building walls for maximizing link performance. A cable connects the unit to an indoor interface terminal that provides Ethernet and VoIP ports.
Fixed/portable indoor units (intergraded antenna, RF baseband and interface in a single box), which are installed indoors close to a window or the outer wall. The unit is portable within the indoor space, however it requires power supply.
Nomadic/mobile units (PCMCIA cards, handheld devices), which are truly portable (mobile in future versions) and can be used in outdoor and indoor spaces.

Figure 1: Abstract of WiMAX deployment scenarios.

Each terminal profile is built with different performance capabilities and cost towards specific customer profiles. Fixed-outdoor terminals are capable of long range, robust links that can transfer high-bandwidth and delay sensitive services with low impact on network air-interface resources, hence they are more suitable for corporate, small-to-medium enterprises (SMEs), and small-offices-home-offices (SOHOs). The higher hardware and installation costs are balanced by higher revenues. Fixed-indoor terminals have considerably less cost and are self-installable, albeit with smaller link range. Such terminals address the mass market of residential access. Finally the nomadic and portable terminals require even greater network design margins and usually address individual customers at specific service areas (such as community/camp networks). Observing Section 1 it can be seen that business customers are likely to prefer a combination of fixed-outdoor and nomadic units, while residential and personal customers will probably select either a fixed-indoor or a nomadic unit. As WiMAX technology progresses, more system gain will be achieved in the air-interface thus resulting in higher cell ranges and increased percentage of nomadic terminals mainly at the expense of fixed-indoor units.

The continuous development of WiMAX technology from IEEE 802.16-2004 standard to the IEEE 802.16e amendment, has led to significant improvements in the air-interface. Recent advances include higher BS transmit power, advanced antenna systems (MIMO, beamforming (BF)), improved radio resource management through the OFDMA profile, improved coding techniques which reduce the signal-to-interference and noise ratio (SINR) thresholds, efficient uplink (UL) subchannelization, and flexible frequency reuse. The current amendment of WiMAX offers more than 15 dB increase in the system gain over previous versions which drastically extends the radio coverage, and can therefore reach indoor customers even when using portable/mobile terminals. As the WiMAX system gain increases due to the continuous enhancement of the air-interface, in the context of dimensioning, the network size for a specific deployment is reduced, and so is the up-front investment.

BENEFITS OF WiMAX

2011-09-07T07:08:00.000-07:00

The WiMAX solution reflects the general trend in the communications industry toward unified packet-based voice and data networks. Fundamental benefits of this transition are reduced operation cost, improved network optimization, and better management of changes. The followings are some of the benefits of WiMAX.

Wireless. By using a WiMAX system, companies/residents no longer have to rip up buildings or streets or lay down expensive cables.

High bandwidth. WiMAX can provide shared data rates of up to 70 Mbps. This is enough bandwidth to support more than 60 businesses at once with T1-type connectivity. It can also support over a thousand homes at 1-Mbps DSL-level connectivity. Also, there will be a reduction in latency for all WiMAX communications.

Long range. The most significant benefit of WiMAX compared to existing wireless technologies is the range. WiMAX has a communication range of up to 40 km.

Multi-application. WiMAX uses the IP and is therefore capable of efficiently supporting all multimedia services from VoIP to high speed Internet and video transmission. It also supports a differentiated QoS enabling it to offer dynamic bandwidth allocation for different service types. WiMAX has the capacity to deliver services from households to small and medium enterprises, small office home office (SOHO), cybercafés, multimedia Tele-centers, schools and hospitals.

Flexible architecture. WiMAX supports several systems architectures, including point-to-point, point-to-multipoint, and ubiquitous coverage.

High security. The security of WiMAX is state of the art. WiMAX supports advanced encryption standard triple data encryption standard. WiMAX also has built-in VLAN support, which provides protection for data that is being transmitted by different users on the same BS. Both variants use privacy key management (PKM) for authentication between BS and SS station. WiMAX offers strong security measures to thwart a wide variety of security threats.

QoS. WiMAX can be dynamically optimized for a mix of traffic that is being carried.

Multilevel service. QoS is delivered generally based on the service-level agreement between the end user and the service provider.

Interoperability. WiMAX is based on international, vendor-neutral standard. This protects the early investment of an operator because it can select the equipments from different vendors.

Low cost and quick deployment. WiMAX requires little or no external plant construction compared with the deployment of wired solutions. BSs will cost under $20,000 but will still provide customers with T1-class connections.

Worldwide standardization. WiMAX is developed and supported by the WiMAX forum (more than 470 members). The WiMAX forum collaborates with different international standards organizations that are developing broadband wireless standards with the intent to provide interoperability among the standards. Some of the other broadband wireless standards include HiperMAN/HiperLAN (Europe) and WiBRO (South Korea). These standards are compatible with WiMAX at the physical layer. WiMAX will become a truly global technology-based standard for broadband and will guaranty interoperability, reliability, and evolving technology and will ensure equipment with very low cost.

VOIP AND IP | WIMAX APPLICATIONS

2011-09-03T03:29:00.000-07:00

The WiMAX standard has been developed to address a wide range of applications. Based on its technical attributes and service classes, WiMAX is suited to supporting a large number of usage scenarios. Table 1 address a wide range of applications.

Table 1: Summary of WiMAX Applications
Class Description	Real Time	Application Type	Bandwidth
Interactive gaming	Yes	Interactive gaming	50–85 Kbps
VoIP, video conferencing	Yes	VoIP	4–64 Kbps
VoIP, video conferencing	Yes	Videophone	32–384 Kbps
Streaming media	Yes	Music/speech	5–128 Kbps
		Video clips	20–384 Kbps
		Movies streaming	>2 Mbps
Information technology	No	Instant messaging	<250 byte messages
		Web browsing	>500 Kbps
		Email (with attachments)	>500 Kbps
Media content download (store and forward)	No	Bulk data, Movie download	>1 Mbps
Media content download (store and forward)	No	Peer to peer	>500 Kbps

Mobile WiMAX is an all-IP network. The use of OFDMA on the physical layer makes it capable of supporting IP applications. It is a wireless solution that not only offers competitive Internet access, but it can do the same for telephone service.

VoIP offers a wider range of voice services at reduced cost to subscribers and service providers alike. VoIP is expected to be one of the most popular WiMAX applications. Its value proposition is immediate to most users. Although WiMAX is not designed for switched cellular voice traffic as cellular technologies as are CDMA and WCDMA, it will provide full support for VoIP traffic because of QoS functionality and low latency. IPTV enables a WiMAX service provider to offer the same programming as cable or satellite TV service providers. IPTV, depending on compression algorithms, requires at least 1 Mbps of bandwidth between the WiMAX BS and the subscriber. In addition to IPTV programming, the service provider can also offer a variety of video on demand (VoD) services. IPTV over WiMAX also enables the service provider to offer local programming as well as revenue generating local advertising.

WIFI Comparison With Wimax

2011-08-30T03:44:00.000-07:00

WiMAX is different from WiFi in many respects. The WiFi MAC layer uses contention access. This causes users to compete for data throughput to the access point. WiFi also has problems with distance, interference, and throughput and that is why triple play (voice, data, video) technologies cannot be hosted on traditional WiFi. In contrast, 802.16 uses a scheduling algorithm. This algorithm allows the user to only compete once for the access point. This gives WiMAX inherent advantages in throughput, latency, spectral efficiency, and advanced antenna support.

Companies developing radical innovations may adopt different stances not only based on the strategic interests of the company but also by taking into other considerations such as the market and its needs and requirements, as well as other products it may carry.

When comparing WiFi and WiMAX, one is comparing their substitutability and complementary to existing technologies and how different companies have and will view them. WiMAX and WiFi can offer some potentially significant cost savings for mobile network operators by providing an alternate means to backhaul BS traffic from cell site to the BS controllers. Mobile network operators typically utilize some type of wired infrastructure that they must buy from an incumbent operator. A WiFi or WiMAX mesh can offer a much more cost-effective backhaul capability for BSs in metropolitan environments.

Using WiFi and WiMAX open broadband wireless standards and implementing mobile computing, governments and partners can quickly and cost-effectively deploy broadband to areas not currently served, with little or no disruption to existing infrastructures. Standards-compliant WLANs and proprietary WiFi mesh infrastructures are being installed rapidly and widely throughout the world. Standards-compliant WiMAX products can provide NLOS backhaul solutions for these local networks and WiMAX subscriber stations can currently provide Internet access to customers such as schools and other educational institutions and campuses.

The results of the comparison show that mobile WiMAX has better performance in all the areas listed above (where it shares performance enhancing features with EVDO and HSDPA/HSPA). Furthermore, the technologies on which mobile WiMAX is based result in lower equipment complexity and simpler mobility management due to the all-IP core network. They also provide mobile WiMAX systems with many other advantages over CDMA-based systems such as

Tolerance to multipath and self-interference
Scalable channel bandwidth
Orthogonal UL multiple access
Support for spectrally-efficient TDD
Frequency-selective scheduling
Fractional frequency reuse
Improved variable QoS
Advanced antenna technology

1XEVDO Comparison With Wimax

2011-08-26T07:03:00.000-07:00

This standard is developed by the third generation partnership project 2 (3GPP2), the body responsible for CDMA and EVDO. 1xEVDO is an enhanced version of CDMA2000-1x. There are four versions that have been released, namely, Rev. 0, Rev. A, Rev. B, and Rev. C.

1xEVDO is a high-speed data only specification for 1.25 MHz frequency division duplex (FDD) channels with a peak downlink (DL) data rate of 2.4 Mbps.

Improvements to CDMA2000-1x in the 1xEVDO Rev. 0 specification include:

DL channel is changed from code division multiplexing (CDM) to time division multiplexing (TDM) to allow full transmission power to a single user.
DL power control is replaced by closed-loop DL rate adaptation.
Adaptive modulation and coding (AMC).
HARQ.
Fast DL scheduling.
Soft handoff is replaced by a more bandwidth efficient “virtual” soft handoff.

1xEVDO Rev. 0, however, was designed to support only packet data services and not conversational services. In 1xEVDO Rev. A and EVDO Rev. C (also dubbed DORC), additional enhancements were added to the 1xEVDO specification. They include the following:

DL: Smaller packet sizes, higher DL peak data rate (up to 3.1 Mbps), and multiplexing packets from multiple users in the MAC layer.
Uplink (UL): Support of HARQ, AMC, higher peak rates of 1.8 Mbps, and smaller frame size

These enhancements in both the UL and DL of 1xEVDO Rev. A allow it to support conversational services.

COMPARISON WITH COMPETING TECHNOLOGIES

2011-08-23T02:22:00.000-07:00

At some point current 2G and 3G network operators will migrate to a 4G network technology. Mobile WiMAX is likely to face competition from 3G and 4G technology enhancements. They include the code division multiple access (CDMA) variants CDMA2000 and wideband-CDMA (WCDMA) and their enhancements which are 1x evolution data optimized (1xEVDO) and HSDPA, respectively. Unlike in the early days of the CDMA vs. GSM competition, this higher generation competition will be quite different and fruitful because for these new generations networks; the applications are separated and do not depend on each other. 4G networks will go far beyond 2G and 3G by mainly improving three parameters:

Interface technology: 4G standards will make a radical change and will use OFDM [9]. The new modulation itself will not automatically bring an increase in speed but very much simplifies the following two enhancements:
Channel bandwidth: 4G systems will use a bandwidth of up to 20 MHz, i.e., the channel offers four times more bandwidth than channels of current systems. As 20 MHz channels might not be available everywhere, most 4G systems will be scalable, e.g., in steps of 1.25 MHz. It can therefore be expected that 4G channel sizes will range from 5 to 20 MHz.
MIMO: The idea of MIMO is to use the multipath phenomena. Although this behavior is often not desired, MIMO makes active use of it by using several antennas at the sender and receiver side, which allows the exchange of multiple data streams, each over a single individual wave front. Two or even four antennas are foreseen to be used in a device. How well this works is still to be determined in practice but it is likely that MIMO can increase throughput by a factor of two in urban environments.

Increasing channel size and using MIMO will increase throughput by about 8–10 times. Thus speeds of 40 Mbps per sector of a cell are thus possible. Using a commonly accepted evaluation methodology for 3G systems, mobile WiMAX has been simulated against the 3G enhancements [23]. These simulations have shown that

Mobile WiMAX peak data rates are up to 5x better than 3G+ technologies.
Mobile WiMAX spectral efficiency is 3x better than any 3G+ technology.
Lower equipment cost for WiMAX due to certified products (compare with WiFi).
WiMAX requires new infrastructure while high-speed packet access (HSPA) rides on UMTS.
Roughly the same coverage (average ~5 km).
Roughly the same performance (average ~2 Mbps per user).
HSDPA launched in 2006 while HSUPA will come in 2008.
WiMAX standard set end of 2005 and first products in 2006.
HSPA has a higher acceptance with mobile operator.

APPLICATION PROVIDERS & WIMAX CUSTOMERS

2011-08-13T09:29:00.000-07:00

APPLICATION PROVIDERS

WiMAX has already revolutionized the broadband wireless market by standardizing broadband wireless access market, by opening up new service opportunities and by creating the environment for ubiquitous broadband services.

The aim is to provide the service that best fits the individual’s needs. Applications can be developed in house by the service providers, outsourced from other companies or developed and sold directly to the end user by an independent applications development company.

Applications are based on IP, and IP applications are sent back or forth via WiMAX. This allows the users to develop applications independently from the underlying network infrastructure. Some applications will still be developed by operators but the vast majority will come from those working directly in the Internet crowd. For them and for the end users competing wireless technologies are very beneficial. Competition spurs network roll outs, offers possibility for new players in the market, and creates competition between device manufacturers. Also, new applications will be introduced more easily and much more quickly as they are no longer forced into a tight framework that takes long time to develop and from which it is difficult to get out again.

WIMAX CUSTOMERS

Prospective WiMAX customers can be grouped either geographically or by the level or volume of services. Geographical categories range from urban to rural customers, while categories according to size include individual customers and the corporate customers. Urban areas offer the highest density of customers with more business establishments. In such cases a higher number of cells which are small in size are required to meet the capacity requirements. These are the areas where more competition is expected. Rural areas are expected to have a lower penetration of customers, less corporate customers, and bigger cell sizes because emphasis is on coverage rather than capacity. Individual subscribers will use WiMAX for music downloads, interactive gaming, and personal broadband Internet, and will form a large percentage the total subscribers.

Corporate subscribers are also expected to contribute to revenues of WiMAX, and their interest will be in applications and services which will enhance their organizations apart from the basic telecommunications services.

Companies are poised to compete with each other in WiMAX network deployment, which will ensure that the prices will be competitive

WIMAX SERVICE PROVIDERS

2011-08-09T03:47:00.000-07:00

As IP networks become faster (higher bandwidth) and more responsive (lower delay), the set of services implemented on IP-based networks has grown. This growth generates more revenue opportunities for service providers, and thus next-generation networks are all migrating toward IP technology. From an operator standpoint, services can be broken into four billable classes:

(1) basic Internet services which are typically billed at a flat rate,

(2) premium Internet services which are important not only to improve ARPU, but to add new services,

(3) VPN services which can be billed by QoS level, and

(4) operator premium services which are applications provided on the operator’s network.

The service providers are expected to gain profits through the sale of the different services and applications that WiMAX is capable of carrying. The different services that can be offered on WiMAX networks include best effort VoIP, carrier class IP telephony through the IP multimedia core, music, video conferencing, streaming video, interactive gaming, mobile instant messaging (IM), IP television (IPTV), basic broadband wireless Internet, and other application-based services to corporate customers. The concept of unbundling the network reduces the barriers of entry into the mobile telecommunications industry because a provider does not need to own the whole network.

The business aspect of the service providers can also be looked at from two perspectives. The first one is where the service provider owns the whole system including the core network and the access network. The second option is the unbundled option where the access network and core networks exist as independent business entities.

In emerging markets such as Africa, and South Asia where telecom investment is still nascent and 3G yet to be launched, WiMAX makes complete business sense even at equal cost, better speeds, better spectrum utilization, and the promise of broadband to a much sparsely spread population. For developed economies, the United States for instance, the 2.3 spectrum band is believed to be more capex efficient and hence better than 3G and high-speed uplink packet access (HSUPA). More importantly, the phase in the capex cycle of a telecom operator will determine each operator strategy—whether to embrace WiMAX or stick to its existing technology. The WiMAX industry entered the year 2007 as a year for ecosystem buildup in the preparation for regional and nationwide deployments of WiMAX services. It appears that 2008 will be a make-or-break period for WiMAX. Figure 1 shows a global WiMAX deployment by region.

Figure 1: Global WiMAX networks. APAC = Asia Pacific, CALA = Caribbean and Latin America.

WIMAX EQUIPMENT VENDORS

2011-08-05T05:44:00.000-07:00

WiMAX, as with many new technologies, is based on an open standard. Although standards increasingly play a crucial role in driving adoption, they are not sufficient to guarantee success. A standard-based technology will success only if a solid ecosystem of operators, vendors, and solution and content providers emerge to support it, as is in the case of WiMAX. WiMAX enables intervendor interoperability which brings lower costs, greater flexibility and freedom, and faster innovation to operators.

Within the WiMAX industry there is a strong commitment to ensuring full interoperability through certification and ad-hoc testing between vendors. It is important for network operators to realize how interoperability is established and what it covers so that they understand how different products, solutions, and applications from different vendors can coexist in the same WiMAX network. The advantages that interoperability brings are multiple. Some of these advantages are the ability to choose among vendors, flexibility when choosing the appropriate network elements and components, success to the latest cutting-edge technology, and an open architecture which makes it easier for operators to roll out new revenue-generation services and applications as they can rely on wider pool of suppliers.

The two categories of equipment vendors include the network equipment vendors and the terminal equipment vendors. Network equipment includes ASN and CSN equipment, and vendors include companies such as Motorola and ZTE of China. They will gain their profits through the sale of the equipment and through installation of the equipment. They may further have after sales agreements with the customers who are the service providers. Terminal equipment includes mobile phones, CPE, modems, laptops, smart phones, and PDAs and they are manufactured by companies like Nokia, Blackberry, Motorola, and Intel. They will gain their profits through the sale of the terminal equipment. Nokia, the world’s top handset maker, expects to start selling cell phones using the WiMAX technology in 2008.

THE WiMAX BUSINESS MODEL

2011-08-01T09:00:00.000-07:00

The biggest challenges to deploying WiMAX-based services are business related. Carriers need financial capability to implement infrastructure. Each operator has to carefully identify its own requirements, dictated by the type of services offered, the market segments targeted, the spectrum available, and the topography of the coverage area. There is no single solution that works for all, and operators need to make key choices about the management and core networks as they plan for their WiMAX networks.

An accurate business case analysis must take into account a wide variety of variables such as demographics, services, frequency band alternatives, capital expense items, operating expense items, and CPE equipment. The WiMAX business model can be looked from several perspectives. These include the equipment vendors, service providers and application providers, and customers. WiMAX will have a larger impact long term than we have seen from cellular phones in the past two decades. Initial rollouts of WiMAX will begin mostly by competitive local phone service carriers and rural Internet service providers. Larger carriers will utilize fixed WiMAX to deliver services to residential customers many of whom are in underserved markets. WiMAX adoption in these underserved markets will be high due to lack of availability of high-speed data access. These deployments will generate capital to be reinvested for future deployments. Larger customer base will begin driving both the cost of carrier and customer equipment down. As the economy of scale makes deployment less expensive, mobile platforms will begin to appear. This development will be spread between high population centers and the rural markets that already have fixed platforms deployed. Fixed platform will act as a springboard for mobile deployment. Then interconnections will begin to form between rural markets and metropolitan markets as carriers from cooperative agreements to share network resources. The economy of scale will increase exponentially at this point and we will notice a negative impact on traditional cellular, Internet, and voice services. Once the implementation of initial hot underserved rural markets and high-density metro areas is completed, springboard deployments will quickly take WiMAX coverage to the level of coverage offered by traditional wireless today. This process will move much faster than the deployment of cellular networks and devices for the following key reasons:

Manufacturing process for WiMAX devices will be quite similar to that of wireless devices and mostly the changes will be in components and software.
Readiness of the current wireless fixed and mobile market and waiting on new technology.
As carriers built out wireless networks, most of the questions in this field have been answered and can now be applied to the development of a mirror network that provides WiMAX access.

NETWORK DIMENSIONING AND DESIGN

2011-07-28T02:26:00.000-07:00

Designing, deploying, and managing any wireless cellular system requires clear objectives to be identified from the outset. These includes definition of the footprint coverage, the estimated number of users, the traffic load distribution, the penetration and growth rate, and internetwork access and roaming. Mobile WiMAX, which will be deployed like 2G and 3G cellular networks, supports fractional frequency. Fractional frequency reuse takes advantage of the fact that mobile WiMAX user transmit on subchannels and does not occupy an entire channel such as in 3G. The objective of the radio network dimensioning and design activity is to estimate the number of sites required to provide coverage and capacity for the targeted service areas and subscriber forecast. This process is based on many assumption such as uniform distribution of subscribers, homogenous morphology, and ideal site location. The main inputs required for network dimensioning are site equipment-specific parameters, marketing-specific parameters, and licenses regulation and propagation models. Figure 1 shows the flow chart of activities performed in network design and planning, starting from data collection of marketing and design requirement input and achieving the business model to provide a nominal site plan using a network simulation software.

Figure 1: The cell planning process.

Mobile WiMAX is designed to complement existing 2G/3G access technologies with an “Always Best Connected” experience with voice and data connections. There is a large range of possible scenarios for the deployment of mobile WiMAX, but main four categories are

Fixed and mobile operator with enhanced data for GSM evolution (EDGE)/3G who uses mobile WiMAX as a complementary extension for data services
Mobile only operator with EDGE/3G who uses mobile WiMAX as a complementary extension for data services
Fixed operator who uses mobile WiMAX to compete with 3G operators for data and voice services
New entrant who uses mobile WiMAX to move into mobile market—threat to incumbent mobile operator.

WiMAX operates in a mixture of licensed and unlicensed bands. The unlicensed bands are typically the 2.4- and 5.8-GHz bands. Licensed spectrum provides operators control over the usage of the band, allowing them to build a high-quality network. The unlicensed band, on the other hand, allows independence to provide backhaul services for hotspots. Typical area licensed WiMAX spectrum allocations are

Lower 700 MHz (US) with 2 × 6 MHz channels
2.5 GHz Multichannel Multipoint Distribution Service with 15.5 MHz in US and 72 MHz in Canada
3.5 GHz Wireless Local Loop with 2 × 2 MHz channel blocks
5.8 GHz UNI (license exempt) with 80 MHz allocation

WiMAX access networks are often deployed in point-to-multipoint cellular fashion where a single BS provides wireless coverage to a set of end users stations within the coverage area. The technology behind WiMAX has been optimized to provide both large coverage distances of up to 30 km under line-of-sight (LOS) situations and typical cell range of up to 8 km under NLOS. In an NLOS, a signal reaches the receiver through reflections, scattering, and diffractions. The signals arriving at the receiver consists of many components from direct and indirect paths with different delay spreads, attenuation, polarizations, and stability relative to the direct path. WiMAX technology solves or mitigates the problem resulting from NLOS conditions by using OFDMA, Subchannelization, directional antennas, transceiver diversity, adaptive modulation, error correction, and power control. The NLOS technology also reduces installation expenses by making the under-the-eaves customer premise equipment (CPE) installation a reality and easing the difficulty of locating adequate CPE mounting locations.

Both LOS and NLOS coverage conditions are governed by propagation characteristics of their environment, radio link budget, and path loss. In both the cases, relays help to extend the range of the BS footprint coverage allowing for a cost-efficient deployment and service.

TECHNOLOGIES EMPLOYED BY WiMAX

2011-07-24T06:36:00.000-07:00

Mobile WiMAX operates in licensed frequency bands in the range of 2 to 6 MHz. The technologies employed by mobile WiMAX include the following:

Scalable orthogonal frequency division multiple access (SOFDMA) on the physical layer
MIMO
IP
Adaptive antenna systems (AAS)
Adaptive modulation schemes (AMS)
Advanced encryption standard (AES) encryption

PHYSICAL LAYER

Mobile WiMAX will initially operate in the 2.3, 2.5, 3.3, and 3.4–3.8 GHz spectrum bands using SOFDMA. OFDMA is perhaps the most important technology associated with WiMAX. SOFDMA is based on OFDMA which in turn is based on OFDM. OFDM is a form of frequency division multiplexing, but it has higher spectral efficiency and resistance to multipath fading and path loss compared to other multiplexing methods. It divides the allocated frequency spectrum into subcarriers which are at right angles to each other. This reduces the possibility of cross-channel interference thereby allowing the subcarriers to overlap. This reduces the amount of frequency spectrum required, hence the high spectral efficiency. The reduced data rate of each stream reduces the possibility of intersymbol interference because there is more time between the arrival of symbols from different paths. This feature of OFDM makes it resistant to multipath fading and ideal for nonline of sight (NLOS) applications. In OFDMA each frequency subcarrier is divided into subchannels which can be accessed by multiple users hence increasing the capacity of OFDM.

Scalable OFDMA is a form of OFDMA which allows variable channel bandwidth allocation from 1.25 to 20 MHz. SOFDMA has capabilities which make it ideal for the implementation of IP and hybrid automatic repeat request (HARQ). WiMAX also uses other features to enhance the performance of OFDMA. They include dynamic frequency shifting, MIMO, AAS, and software-defined radios. Dynamic frequency shifting monitors the signal and changes frequencies to avoid interference. Software-defined radios are controlled by changing software settings and this gives the equipment more flexibility when switching frequencies.

MIMO is a technology that has already found use in WiFi (IEEE 802.11n). MIMO multiplies the point-to-point spectral efficiency by using multiple antennas and RF chains at both the BS and the MS. MIMO achieves a multiplicative increase in throughput compared to single-input, single-output (SISO) architecture by carefully coding the transmitted signal across antennas, OFDM symbols, and frequency tones. These gains are achieved at no cost in bandwidth or transmit power.

AAS are spatial processing systems which combine antenna arrays with sophisticated signal processing. They reduce the effects of interference from multiple signal paths thereby also contributing to high capacity of the system and the use of mobile WiMAX in NLOS environments.

MAC SUBLAYER

The 802.16 MAC sublayer uses a scheduling algorithm for which the subscriber station only needs to compete for initial entry into the network. The scheduling algorithm also allows the BS to control QoS parameters by balancing the time-slot assignments among the application needs of the subscriber stations.

WiMAX supports QoS differentiation for different types of applications. The 802.16 standard defines the following types of services:

Unsolicited grant services (UGS): UGS is designed to support constant bit rate (CBR) services, such as T1/E1 emulation, and Voice-over-IP (VoIP) without silence suppression.
Real-time polling services (rtPS): rtPS is designed to support real-time services that generate variable size data packets on a periodic basis, such as MPEG video or VoIP with silence suppression.
Nonreal-time polling services (nrtPS): nrtPS is designed to support nonreal-time services that require variable size data grant burst types on a regular basis.
Best effort (BE) services: BE services are typically provided by the Internet today for Web surfing.

CORE SERVICES NETWORK | WIMAX NETWORK ARCHITECTURE

2011-07-20T05:50:00.000-07:00

The CSN is the transport, authentication, and switching part of the network. It represents the core network in WiMAX. It consists of the home agent (HA) and the AAA system and also contains the IP servers, gateways to other networks, i.e., public switched telephone network (PSTN), and 3G.

WiMAX has five main open interfaces which include reference points R1, R2, R3, R4, and R5 interface. The R1 interface interconnects the subscriber to the BS in the ASN and is the air interface defined on the physical layer and Medium Access Control (MAC) sublayer. The R2 is the logical interface between the mobile subscriber and the CSN. It is associated with authorization, IP host configuration management, services management, and mobility management. The R3 is the interface between the ASN and CSN and supports AAA, policy enforcement, and mobility management capabilities. The R4 is an interface between two ASNs. It is mainly concerned with coordinating mobility of MSs between different ASNs. The R5 is an interface between two CSNs and is concerned with internetworking between two CSNs. It is through this interface that activities such as roaming are carried out.

The unbundling of WiMAX divides the network based on functionality. The ASN falls under the network access provider (NAP). The NAP is a business entity that provides WiMAX network access to a network service provider (NSP). The NSP is a business entity that provides core network services to the WiMAX network and consists of the CSN. The Applications services fall under the applications service provider (ASP).

If network operator wants to reap the full benefits that WiMAX and its all-IP architecture can deliver, they need to carefully select the ASN and CSN solutions that best suit their requirements and provide all the functionality required while avoiding unnecessary complexity in their network.

ACCESS SERVICES NETWORK | WIMAX NETWORK ARCHITECTURE

2011-07-17T04:52:00.000-07:00

The ASN is the access network of WiMAX and it provides the interface between the user and the core service network. Mandatory functions as defined by the WiMAX forum include the following:

Handover
Authentication through the proxy authentication, authorization, and accounting (AAA) server
Radio resource management
Interoperability with other ASN’s
Relay of functionality between CSN and mobile station (MS), e.g., IP address allocation

Base station (BS): The cell equipment comprises the basic BS equipment, radio equipment, and BS link to the backbone network. The BS is what actually provides the interface between the mobile user and the WiMAX network. The coverage radius of a typical BS in urban areas is around 500–900 m. In rural areas the operators are planning cells with a radius of 4 km. This is quite a realistic number now and quite similar to the coverage areas of GSM and UMTS high-speed downlink packet access (HSDPA) BSs today.

Deployment is driven either by the bandwidth required to meet demand, or by the geographic coverage required to cover the area. Based on the cell planning of other previous technologies, urban and suburban segments cell deployment will likely be driven by capacity. Rural segment deployment will likely be driven by the cell radius. For BTS systems, the emphasis is more on performance than on cost and size, although there still is an interest in low cost because WiMAX is a new deployment.

ASN gateway: The ASN gateway performs functions of connection and mobility management and interservice provider network boundaries through processing of subscriber control and bearer data traffic. It also serves as an Extensible Authentication Protocol (EAP) authenticator for subscriber identity and acts as a Remote Authentication Dial-In User Service (RADIUS) client to the operator’s AAA servers.

WIMAX NETWORK ARCHITECTURE

2011-07-13T09:10:00.000-07:00

The mobile WiMAX end-to-end network architecture is based on an All-Internet Protocol (IP) platform, all packet technology, and no circuit switch telephony. The end-to-end architecture makes the greatest possible use of IETF and IEEE standards and protocols along with the adoption of commonly available standard equipment.

The open IP architecture gives network operators great flexibility when selecting solutions that work with legacy networks or that use the most advanced technologies, and in determining what functionality they want their network to support. They can choose from a vertically integrated vendor that provides a turnkey solution or they can pick and choose from a dense ecosystem of best-of-breed players with a more narrow focus. The architecture allows modularity and flexibility to accommodate a broad range of deployment options such as small scale to large scale, urban, suburban, and rural coverage, mesh topologies, flat, hierarchical and their variant, and finally, coexistance of fixed, nomadic portable and mobile usage models.

Mobile WiMAX adds both the mobility and multiple-input multiple-output (MIMO) functionalities to the IEEE 802.16-2005 standard. It is one of two standards adopted by the WiMAX forum with the other one being the IEEE 802.16-2004. Mobile WiMAX network architecture mainly has three components. These include the access services network (ASN), the core services network (CSN), and the application services (AS) network. Figure 12.1 illustrates the interconnection of these networks. The WiMAX network supports the following key functions:

All-IP access and core service networks
Support for fixed, nomadic, and mobile access
Interoperability with existing networks via internetworking functions
Open interfaces between ASNs and between the ASN and the CSN
Support for differential quality of service (QoS) depending on the application
Unbundling of the access, core, and application service networks

Figure 1: WiMAX network architecture.

WiMAX—Architecture, Planning, and Business Model

2011-07-01T00:59:00.000-07:00

The fixed/portable broadband wireless access is becoming a necessity for many residential and business subscribers worldwide. The demand is exploding as the pricing of broadband services is rapidly decreasing. The worldwide interoperability for microwave access (WiMAX) technology is an integral part of the portfolio by complementing 2G/3G mobile access, Digital Subscriber Line (DSL) broadband fixed access, and Wireless Fidelity (WiFi) hotspot access. An extended overview of WiMAX and its applications in higher generation wireless networks as a cost-effective solution to answering the challenges posed by the digital divide is presented. Technology behind WiMAX and its network architecture, design, and deployment are examined in addition to factors that impact WiMAX planning and performance. A WiMAX radio coverage simulation and analysis at different frequency bands for different demographic is presented. Furthermore, the WiMAX business models and a comparison with two enhanced third-generation (3G) technologies that are potential competitors to WiMAX are explored.

Telecommunications has grown at a tremendous rate in the last ten to twenty years. Improved semiconductor and electronics manufacturing technology, and the growth of the Internet and mobile telecommunications have been some of the factors which have fueled this growth in telecommunications. The deployment of state-of-the-art telecommunications infrastructure and services has, however, been restricted to the developed world. The least developed countries have been left in the technological dark ages with few or none of the next-generation networks installed. Developed countries now boast high-speed connections with a large percentage of homes having access to the Internet and broadband services at an affordable fee. The underdeveloped countries are yet to enjoy such facilities. This is referred to as the digital divide. During the first World Summit on the Information Society (WSIS) held in Geneva in December 2003, the Digital Divide was defined as the unequal access to information and communication technologies (ICTs), where the least developed countries are separated from the developed countries because of a lack of technology particularly ICT.

The digital divide has persisted due to the relatively high cost of putting up modern telecommunications infrastructure. This is compounded by the fact that there are a number of different services available and each service requires its own technology and network. Therefore, existing technologies such as Wireless Fidelity (WiFi), Digital Subscriber Line (DSL), Global System for Mobile communications (GSM), Integrated Services Digital Network (ISDN), and the relatively new 3G technologies have not been able to provide a total solution to closing the digital divide. Figure 1 illustrates the main network types and the prevalent technologies associated with each, mapped against usage models and access modes.

Figure 1: Wireless network type and range. MAN—metropolitan area network (citywide, rural area), LAN—local area network (office, home, campus), and WAN—wide area network (countrywide, international).

WiMAX will boost today’s fragmented broadband wireless access market and mobile WiMAX promises to offer a solution to closing the existing digital divide. WiMAX can address the fixed wireless access and portable Internet market, complementing other broadband wireless technologies. Government initiatives to reduce the digital divide are making gains for broadband wireless countries such as Australia, South Korea, Taiwan, and the United States have programs in place today, and there has been a push by the European Commission for more flexible spectrum policies.

WiMAX access can be easily integrated within both fixed and mobile architectures, enabling operators to integrate it within a single converged core network, thereby providing new capabilities for a user-centric broadband world.

WiMAX addresses the following needs which may answer the question of closing the digital divide:

Cost effective
Offers high data rates
Supports fixed, nomadic, and mobile applications thereby converging the fixed and mobile networks
Easy to deploy and has flexible network architectures
Supports interoperability with other networks
Aimed at being the first truly a global wireless broadband network

WiMAX is a standard that is championed by the WiMAX forum which was formed in June 2001 to promote conformance to IEEE 802.16 standard. The WiMAX forum currently has more than 470 members comprising the majority of operators, component, and equipment companies in the communications ecosystem. The WiMAX forum promotes interoperability by working closely with IEEE and other standards groups such as the European Telecommunications Standards Institute (ETSI) which have their own versions of broadband wireless. Along these lines, the WiMAX forum works closely with service providers and regulators to ensure that WiMAX forum certified systems meet customer and government requirements.

The original WiMAX standard only catered for fixed and nomadic services. It was reviewed to address full mobility applications; hence, the mobile WiMAX standard, defined under the IEEE 802.16e specification was created. Mobile WiMAX supports full mobility, nomadic, and fixed systems to compete against DSL to cover isolated areas such as rural hot spots, private campus networks, and remote neighborhoods. Mobile WiMAX is more promising to be deployed as a cellular network that offers ubiquitous broadband services to mobile users to over large geographical areas. It can be deployed as a central office bypass to avoid using existing wired infrastructure for competitive local exchange carriers and wireless Internet service provider. Figure 2 shows the standard history for 802.16.

Figure 2: 802.16 standard evolution.

ROUTING ALGORITHMS | WiMAX Mesh Networks

2011-06-29T07:27:00.000-07:00

In this section we will investigate routing and scheduling algorithms for WiMAX mesh networks, with the target application of using WiMAX mesh networks to network cellular BSs and gateways to the Internet. The gateways of general cellular networks can be mobile switching center (MSC) in GSM networks or MSC and serving GPRS support node (SGSN) in WCDMA networks. In this network architecture, the gateways act as 802.16 mesh BSs in the 802.16 backhaul network. They manage the backhaul network and allocate bandwidth to the SSs. The cellular BSs act as 802.16 SSs in the backhaul network. In the remaining part of the chapter, the cellular BSs will be called SSs for simplicity. The SSs forward aggregated traffic from the mobile users to Internet in the uplink direction and deliver traffic to the mobile users from the Internet in the downlink direction via the 802.16 BSs. For simplicity, only uplink traffic with QoS requirement is considered in this chapter. However, the approaches of routing and scheduling can be applied to bidirection traffic. It is assumed that the backhaul topology and traffic demands from the SSs will not change frequently. Therefore, the frequency of updating traffic routes and scheduling will be low. It is feasible and beneficial to use high performance algorithms such as the optimal centralize scheduling and routing algorithms.

CLASSIFICATION

As IEEE 802.16 standards specify the MAC and PHY layer protocols, routing protocols are outside the scope of the standard work. We can identify the following ways of solving routing and scheduling (bandwidth allocation) problems for WiMAX mesh networks:

Centralized routing and centralized scheduling (CRCS): All the traffic, position information of the stations are sent to the BSs. The BSs jointly solve the routes and schedule problems, and send the routes/schedule information back to the SSs.
Distributed routing and centralized scheduling (DRCS): SSs distributedly find their routes to the BSs. After the routes are found, the SSs send their information to the BSs. The BSs determine collision-free transmission schedule.
Distributed routing and distributed scheduling (DRDS): Stations distributively find their routes to the BSs. After SSs find their routes to BSs, they work with their next-hop stations toward the BSs to determine collision-free transmission schedules in a distributed way.
Hybrid routing and scheduling (HRS): Both routing and scheduling algorithms can be implemented in either a distributed or a centralized way.

In WiMAX mesh networks, distributed scheduling can be used by SSs with their neighbor SSs to reserve time slots. However, the success of reserving time slots will depend on the availability of free time slots not reserved by the centralized scheduling and other neighbor stations. As centralized scheduling has priority over distributed scheduling in WiMAX, it will be easier to provide time slots reservation for end-to-end QoS support. Therefore, we will consider only centralized scheduling in this chapter.

Centralized scheduling algorithm will work together with either centralized or distributed routing algorithms. For the centralized routing algorithm, routing, bandwidth allocation, and scheduling problem can be jointly investigated. We will formulate the joint routing, allocation, and scheduling problem with an optimal mathematical model. The input to the optimal model from the routing point of view will be network connectivity matrix CM, CM_ij = 1 if and only if station i is in the communication range of station j. After the optimal problem is solved, the route from an SS to a BS will be determined.

For the centralized scheduling and distributed routing algorithms, routes from SSs to BSs will be determined first. Then we can also derive network connectivity matrix CM for distributed routing. However, in this case, the station connectivity matrix CM will not only be determined by the communication ranges but also the traffic routes. We have CM_ij = 1 if and only if the link e_ij between station i and station j is in any route of SSs to BSs. Then the connectivity matric CM can be input to the optimization model for centralized scheduling.

DISTRIBUTED ROUTING

In the WiMAX mesh networks, the BSs will periodically broadcast network configuration messages, which are further forwarded by the neighbor stations. The forwarding process continues toward network edge until the predefined maximum number of hops is reached. For each forwarding neighbor stations, it will add the information of hops from itself to the BSs. Although routing algorithm is not specified in the standards, the distance information together with other local information can be utilized by a new SS to find a route to a BS.

For a new SS to join the WiMAX mesh network, it is required to listen to the network configuration/synchronization from its neighbor stations. After it hears network configuration message at least twice from a station, it can send request to join the network through this station. With the available local information, such as the signal strength, distance between, the new station can select which station to be its sponsor station to a BS. Therefore the distributed routing algorithm can be reduced to find a sponsor station for a route toward a BS. We simply give four methods.

Random selection (RS): In this method, a new station will choose a neighbor station as its sponsor station randomly among the candidate stations with the same minimum number of hops to a BS.
Minimum node ID (MID): Among all the neighbor stations with the same minimum number of hops to any BSs, the station with minimum node ID will be selected as the sponsor station.
Maximum signal strength (MSS): The station with maximum average signal strength will be selected as the sponsor station among the candidate stations, which can achieve higher transmission data rate.
Minimum aggregate traffic (MAT): To balance traffic routed over the neighbor stations, a new station can also choose the station with minimum aggregated traffic load among the candidate stations.

LITERATURE SURVEY

2011-06-26T03:45:00.000-07:00

An in-depth survey which covers the research activities and open issues from physical layer to applications, and from mobility management to power management, from designs with single channel single radio device to those with multiple channel multiple radio devices. However, most of the existing researches about wireless mesh networks are based on IEEE 802.11 standard. The routing and MAC layer protocols designed for 802.11 standard-based wireless mesh network cannot be used directly or efficiently for WiMAX mesh networks. And not much work has been done on WiMAX mesh networks. In this section, we will introduce the routing and scheduling related work on WiMAX mesh networks.

COORDINATED DISTRIBUTED SCHEDULING

The main idea of the coordinated distributed scheduling is to coordinate the transmission of MSH-DSCH messages over transmission opportunities in a collision-free manner. Through the exchanges of collision-free MSH-DSCH over control subframes, collision-free data slot reservations in the data subframes can be achieved.

To achieve the goal of collision-free MSH-DSCH transmission, nodes will exchange 2-hop or 3-hop neighborhood scheduling information with each other. Because nodes shall run the scheduling algorithm independently, a common algorithm has been specified in the standard for each node in the neighborhood to calculate the same schedule. The algorithm is random and predictable by dynamically constructing the seeds of a random number generator for each node according to a common rule. In particular, the seed for a given node is constructed based on its unique node ID and the index of the candidate transmission opportunity. The most important parameters that have significant impacts on the performance of coordinated distributed scheduling are Xmt Holdoff Exponet (3 bits) and Next Xmt Xm (5 bits). They can be used to control the contention on the transmission opportunities and improve bandwidth utilization.

Due to the importance of the coordinated distributed scheduling on the network performances, an analytical framework is needed to assess the performance of the scheduling scheme. Cao et al. analytically investigated how the channel contention is correlated with the total node number, exponent value, and network topology. With the assumption that the transmit time sequences of all the nodes in the control subframe form statistically independent renewal processes, they developed methods for estimating the distributions of the node transmission interval and connection setup delay. The analytical method will be helpful for evaluating upper layer performance like throughput and delay. They implemented the coordinated distributed scheduling module in NS-2 and showed that their analytical model is quite accurate under various scenarios, including both single hop and multihop networks.

Based on Cao’s analytical model, Bayer et al. presented an enhancement of the model. In particularly, they evaluated the scalability of the coordinated distributed scheduling. A scalability problem was observed that leads to poor performance in dense networks and aggravates QoS provisioning. The problem may result from the election-based transmission timing mechanism for scheduling the transmission of MSH-DSCH messages. They propose a dynamic adaptation mechanism to counteract the scalability problem, in which the parameter Xmt Holdoff Exponet is dynamically and locally adjusted according to the network contention and the status of a node. The Next Xmt Xm is used as a contention indicator. If Next Xmt Xm used by the node or its neighbors exceeds a specified threshold, the Xmt Holdoff Exponet is increased. The status of a node is defined according to its transmission activity, if it is BS or if it is a sponsor node. Significant UDP throughput increase is observed with the application of the adaptation mechanism for both single and multiple hop network scenarios.

In the 802.16 standard and the above analytical work, it is assumed that the control messages can be transmitted without collision in the extended neighborhood (2-hop or 3-hop). However, such kind of interference model may not hold in practice. Zhu and Lu investigate the performance of coordinated distributed scheduling under a realistic interference model [12]. Extensive simulations were conducted to evaluate the reception collision performance of the scheduling mechanism. It was reported that the collision ratio of control messages can be as high as 20 percent for 2-hop extended neighborhood. They studied how to deal with the collision problem by appropriate configuration of parameters such as Xmt Holdoff Exponent.

MESH NETWORK ENTRY MECHANISM

2011-06-22T02:22:00.000-07:00

The standard specifies network entry mechanism to find sponsor nodes and establish links with neighbors. In this chapter, the network entry mechanism is used as one of the alternatives to establish traffic routes for WiMAX mesh networks. Upon entering the mesh network, a new SS searches for MSH-NCFG to acquire coarse synchronization with the network. Once the physical layer has achieved synchronization, the MAC layer can acquire network parameters for the MSH-NCFG message. Meanwhile, the SS can builds a physical neighbor list, from which the SS can select a potential sponsor node out of the eligible sponsor nodes. How to select the sponsor node is not specified in the standard. The selection method will be discussed in more details later in this chapter. The SS then synchronizes its time to the potential sponsor node and sends a network entry request message to the potential sponsor node. If the candidate sponsor node accepts the request and opens a sponsor channel, the channel is ready for use to register with the BSs. After the SS is authorized to enter the network by the BS, it can request bandwidth from the BS via the sponsor node and can also establish links with the SSs other than the sponsor node.

BANDWIDTH ALLOCATION AND GRANT MECHANISMS

In the 802.16 standard, flexible bandwidth allocation and grant mechanisms have been defined for PMP mode to guarantee the QoS of various service flows. Those mechanisms include periodically polling, real-time polling, nonreal-time polling, contention-based bandwidth request scheme, poll-me bit, bandwidth stealing, and piggyback. However, the bandwidth request and grant mechanisms can not be used in Mesh mode due to the multihop networking in Mesh mode. In WiMAX Mesh mode, all the communications in the links in the network are controlled by three ways, i.e., using a centralized scheduling algorithm, using a distributed scheduling algorithm within each node’s extended neighborhood, or using a combination of these two types of algorithms.

In the coordinated distributed scheduling algorithm, all stations (BS and SSs) coordinate their transmissions in their extended two-hop neighborhood. Coordinated distributed scheduling does not rely on the operation of a BS and transmissions are not necessarily directed to or from the BS. Within the constraints of the coordinated schedules, uncoordinated distributed scheduling can be used for fast and ad-hoc setup of schedules on a link-by-link basis with directed requests. Grants of the uncoordinated schedules need to ensure that the resulting data transmissions do not cause collisions with the data and control traffic scheduled by the coordinated scheduling algorithms.

Although distributed scheduling algorithms are more scalable, they are inefficient in QoS guarantee. On the other hand, centralized scheduling ensures collision-free scheduling over the links in the network, typically in a more optimal manner than the distributed scheduling method. Therefore better QoS support and network bandwidth utilization can be achieved. In this chapter centralized scheduling will be the only scheduling algorithm studied.

Centralized Scheduling

With the centralized scheduling algorithm, transmission schedule for the SSs is defined by the BS. The BS determines the flow assignments from the resource requests from the SSs. Subsequently, the SSs validate the MSH-CSCH schedule and determine the actual schedule from these flow assignments. The assignments determined by the BS extends to those SSs not directly connected to the BS. Intermediate SSs are responsible for forwarding bandwidth requests for SSs listed in the routing tree that are further from the BS (i.e., more hops from the BS) and the MSH-CSCH message from the BS to their neighbors as required. The SS resource requests and the BS assignments are both transmitted during the schedule control subframe. Centralized scheduling ensures that transmissions are coordinated to ensure collision-free scheduling over the links in the routing tree to and from the BS. The centralized scheduling will persist over a duration that is greater than the cycle time to relay the new resource requests and distribute the updated schedule.

Determination of the flow assignment and routing tree is outside the scope of the standard.

Distributed Scheduling

Coordinated distributed scheduling ensures that transmissions are scheduled in a manner that does not rely on the operation of a BS, and that is not necessarily directed to or from the BS. In the coordinated distributed scheduling mode, all the stations (BS and SSs) need coordinate their transmissions in their extended two-hop neighborhood. The coordinated distributed scheduling mode uses some or the entire control portion of each frame to regularly transmit its own schedule and proposed schedule changes on a PMP basis to all its neighbors. Within a given channel all neighbor stations receive the same schedule transmissions. All the stations in a network have to use the same channel to transmit schedule information in a format of specific resource requests and grants.

Within the constraints of the coordinated schedules (distributed or centralized), uncoordinated distributed scheduling can be used for fast, ad-hoc setup of schedules on a link-by-link basis. Uncoordinated distributed schedules are established by directed requests and grants between two nodes, and shall be scheduled to ensure that the resulting data transmissions (and the request and grant packets themselves) do not cause collisions with the data and control traffic scheduled by the coordinated distributed nor the centralized scheduling methods.

The major differences between coordinated and uncoordinated distributed scheduling are in the portions where the MSH-DSCH messages are transmitted. In the coordinated case, the MSH-DSCH messages are scheduled in the control subframe in a collision-free manner. In the uncoordinated case, MSH-DSCH messages are transmitted in data subframes and may collide.

Routing and Scheduling for WiMAX Mesh Networks

2011-06-18T09:13:00.000-07:00

In addition to the mandatory implementation of point-to-multipoint (PMP) mode, WiMAX networks can be optionally configured to work in Mesh mode, to achieve increased reliability, coverage, and reduced network costs. Although IEEE 802.16 standards specify several quality of service (QoS) schemes and related message formats for WiMAX networks, the problems of scheduling algorithms for both PMP and Mesh mode are left unsolved. Routing algorithms for WiMAX networks are outside the scope of the standard work as well. In this chapter, we investigate the issues of routing and scheduling for WiMAX mesh networks. We overview the mesh mechanisms specified in the IEEE 802.16 standard and survey the existing research on scheduling and routing for WiMAX mesh networks. Then both distributed and centralized routing algorithms are studied, and their effectiveness on alleviating potential network congestion is compared. The scheduling problem is mathematically modeled by taking into account the interference constraints. Solutions are developed which can maximize the utilization of network capacity subject to fairness constraints on the allocation of scarce wireless bandwidth.

INTRODUCTION

In today’s telecommunications, networking and services are changing in a rapid way to support next generation Internet (NGI) user environment. Wireless networks will play an important role in NGI. Wireless broadband networks are being increasingly deployed and used in the last mile for extending or enhancing Internet connectivity for fixed or mobile clients located on the edge of the wired network.

With high data rate, large network coverage, strong QoS capabilities, and cheap network deployment and maintenance costs, WiMAX is regarded as a disruptive wireless technology and has many potential applications. It is expected to support business applications, for which QoS support will be a necessity. Depending on the applications and network investment, WiMAX network can be configured to work in different modes, point-to-multipoint (PMP) or Mesh mode. An illustration of PMP and mesh mode is presented in Figure 1. For example, the network can have a simple base station (BS) working in PMP mode and serving multiple subscriber stations (SSs) if the potential SSs can be covered by the BS. Mesh topology is an optional configuration for WiMAX networks. In the Mesh mode, traffic demands are aggregated at a set of SS nodes which are equipped with 802.16 interfaces. Subsequently, the traffic demands at SS nodes are delivered to a set of BSs nodes which functions in the PMP mode. These BS stations can be connected by a backhaul and connected to Internet access point (IAP) nodes. An amendment to the IEEE 802.16 specifications (where WiMAX is based) is IEEE 802.16j, Multihop Relay Specification. IEEE 802.16j is being developed by IEEE 802.16’s Relay Task Group. It is expected to extend reach/coverage through relaying.

Figure 1: WiMAX (a) PMP network and (b) mesh network architectures.

Wireless mesh network offers increased reliability, coverage, and reduced network costs. There are extensive research, standardization, and commercial development activities on mesh networks. For example, several IEEE special task groups have been established to define the requirements for mesh networking in wireless personal area networks (WPANs), wireless local area networks (WLANs), and wireless metropolitan area networks (WMANs). A brief description of the standardization activities. Wireless mesh networks can be a prospective solution for broadband wireless Internet access in a flexible and cost-effective manner. However, wireless mesh networks also raise a number of research challenges, e.g., network routing, scheduling, QoS support, network management, etc. Those challenges are faced by WiMAX mesh networks without exception. WiMAX Mesh mode is defined with OFDM for frequency between 2 and 11 GHz and time division multiple access (TDMA) is used in the Medium Access Control (MAC) layer to support multiple users. Unlike the single hop wireless networks, routing algorithms are required to determine routes for the connections between a SS and a BS. As WiMAX networks operate synchronously in a time-slotted mode, it is also necessary to allocate time slots without collision over the network to achieve assigned bandwidth for each connection. More challenging is that the routing and scheduling for WiMAX networks are tightly coupled. The routing and scheduling problem for WiMAX networks is different from 802.11-based mesh networks. In the 802.11-based mesh networks, the MAC layer is contention-based; routing algorithms and MAC layer protocols can be designed and operated separately.

Although IEEE 802.16 standards specify several QoS schemes and related message formats, the problems of scheduling algorithms for both PMP and Mesh mode are left unsolved. Routing algorithms for WiMAX networks are outside the scope of the standard work as well. In this chapter, we will investigate the issues of routing and scheduling in WiMAX mesh networks. Both distributed and centralized routing algorithms will be studied, and their effectiveness on alleviating potential network congestion will be compared. The scheduling problem will also be mathematically modeled by taking into account the interference constraints. Solutions are developed to maximize the utilization of network capacity subject to fairness constraints on allocation of scarce wireless resource among the SSs.

CONCLUSIONS AND RECOMMENDATIONS | Relay-Assisted Mobile WiMAX

2011-06-15T01:39:00.000-07:00

Achieving high throughputs with low outage probability in a large cell is a challenge, but this drives effective deployment with MIMO and multihop relay techniques, which have been intensively explored in this chapter. We have analyzed the effective relay efficiency. For relay systems without radio resource sharing, a higher relay SNR gain was required. This implies that the system requires a higher transmit power at the RS. In contrast, radio resource sharing offers high potential for a mobile WiMAX network when relays are deployed. Radio resource sharing is very applicable to MIMO relaying. In addition to multiuser transmission, the relay with radio resource sharing has the potential to achieve optimal multiuser relay gain.

To achieve the practical application of radio resource sharing, a relay system must be based on a topology that fully exploits effective resource assignment based on the spatial separation of nodes. Directional distributed relay topologies were introduced for highly efficient relay deployment. This scheme is fully backward compatible with the current mobile WiMAX standard.

Greater efficiencies in spectrum use can be achieved by coupling channel-quality information in the resource-allocation process. However, this study is heavily dependent upon realistic application environments. Channel modeling becomes critical to reflect application environments. With this purpose, a multihop relay measurement campaign was performed and is presented in this chapter. The measurement results have been analyzed and compared with several statistical and standardized models. It was found that the 802.16d model only matched well to the BS–RS link in terms of path loss and antenna-height correction factor. For the BS–MS link, the WINNER models were a good match to the shadowing, and the COST 231 Hata and WI models were useful for predicting path loss. The RS–MS link was also well matched by the WINNER shadowing values, but none of the path-loss models considered were a good match to any of individual measurement locations.

To demonstrate the potential of the directional distributed relay architecture in a realistic outdoor environment, a site-specific ray-tracing propagation model is used. Results show that, compared to a relay system without resource sharing, the implementation of resource sharing improves capacity significantly. For the OFDMA multiuser transmission, it is ideal to adopt flexible channelization and MIMO LA on different users, and multihop relay with efficient radio resource management.