WIMAX NETWORK PLANNING PROCESS

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

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

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.