QUALITY, CAPACITY, AND ECONOMIC ISSUES OF NETWORK DESIGN

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

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

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

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

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

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

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