Optimization phase | Network Planning

Once the planning and implementation are complete, it is very common practice to run drive tests for the planned sites. Drive tests verify the predictions made by the planning tools, and the results from the drive tests are compared against the results from the simulations. Fine tuning is performed after the drive tests to ensure that the deviation in the results between simulation and drive tests is minimal.

A part of the drive test, parameters like reference signal receive power (RSRP), reference signal received quality (RSRQ), or SINR the UL and DL throughputs at different points of the cell are noted. The results are then compared with the SNR predictions made by the planning tool and deviations are noted and tuned wherever required.

Coverage Planning

Coverage planning targets for the complete service area are tested to ensure there are no coverage holes (i.e., the UE never experiences a no-service condition within the entire service area). Coverage plans, however, do not take into consideration any quality of service that the user experiences within a cell or site. The end aim is to provide the count of the resources or eNodeBs and cells that are required for the complete service area.

Some of the most important aspects that need to be considered as a part of the coverage planning are:

1.       The eNodeB transmitting power and the type of cell that is being planned. The eNodeB transmitting power is the key for any coverage planning, and the transmitting power will vary based on the cell size. For example, a macro cell will have a transmission power of 10 watts per port (40 watts per cell in cases of MIMO cells). The DL coverage cell radius should be derived based on the transmission power of the antenna added with the gains (antenna gain, diversity gain, etc.) with the assumption of path loss (receiver loss, propagation loss, etc.). Cell radius calculation will be covered in detail in the link budget calculation section.

2.       The eNodeB receiver sensitivity. In the uplink, in order to calculate the cell radius, one of the most important parameters that the operator relies on is the receiver sensitivity of the eNodeB. The eNodeB receiver sensitivity is a deciding factor for the maximum allowed path loss between the UE and the eNodeB in the uplink direction, beyond which the eNodeB cannot differentiate accurately between signal and noise. Better receiver sensitivity of the eNodeB will directly result in a larger cell radius (coverage radius) in the uplink. The 3GPP 36.141 defines the test for deriving the reference sensitivity of a receiver.

The specification also requires that a receiver sensitivity of less than -100.8 decibel milliwatts (dbm) is acceptable. However, many vendors have a receiver sensitivity value of around -102 dbm or better.

3.       UE receiver sensitivity and transmission power. Similar to the eNodeB receiver sensitivity, UE receiver sensitivity is an important factor in determining the DL cell radius for coverage planning. Typically for a macro cell, the UL cell radius will be a limiting factor in comparison with the DL cell radius simply because of the difference in the transmission powers. In LTE category 2 UE and onward, the maximum uplink transmit power is 23 db.

4.       Terrain. Terrain is an important consideration for any site planning and will impact the absorption or attenuation capability of a site. For example, a site with irregular heights will not have linear loss and is subjected to shadow areas or reflection, whereas a site with fairly regular height will have a more predictable linear loss. Similarly, the indoor to outdoor ratio of a site also makes a difference when it comes to cell radius calculation (i.e., the penetration losses for an indoor user is higher compared with that for an outdoor user); therefore, planning an urban cell will be subject to more losses due to a higher percentage of indoor to outdoor users in comparison with a rural cell.

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.