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.