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.
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
|
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.
