# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 5 Results and Discussion  
Another observation about developing countries is the sparse settlement pattern and the heightened demand for ICT infrastructure development with the capacity to cover more areas with less deployment. Most settlements are 10 to 40 km away from urban areas, so whether sparse settlement patterns connectivity and access are addressed by the deployment of WiMAX 802.16e was analyzed.  
Figure 2: Simulation model.  
The simulation results depended on the simulation model, which evaluated the performance of WiMAX involving subscriber stations placed at various distances from the base station, at 10 km, 20 km, and 30 km.  
Figure 3 shows Subscriber Station 1, at 10 km; Subscriber Station 8, at 20 km; and Subscriber Station 11, at 30 km. Again, the same amount of \(Y\)-axis packets sent across the network and the simulation time (\(X\)-axis) was equal for the three scenarios.  
Figure 4 shows traffic received, with slight variations for the three subscriber stations. For example, packets received at 10 km and 20 km had very slight variations, while packets received at 30 km were significantly different. For 20 km, the rate of packets received widened, implying that distance affects the quality of the packets received and the variations caused by a signal blockage--trees or anything else that blocked the Line of Sight. In the rural area transmission had an increased use of nodes and lower traffic to manage variations--the latter is a common feature in rural areas.  
Figure 4: Traffic received after simulation.  
Figure 3: The initial traffic sent.  
Figure 5 presents the average time delay of traffic from the base station to the three subscriber stations. The delay for the different subscriber stations varied: the subscriber station at 10 km experienced a more negligible time delay, while the subscriber station at 20 km experienced an average delay. In comparison, the subscriber station at 30 km experienced the most significant time delay.  
In Figure 6, the average time jitter for traffic over the network upon reaching the three subscriber stations shows that the subscriber station at 30 km experienced jitter, even though it was low. In addition, the subscriber stations at 10 km and 20 km also experienced jitter, although less than the subscriber station at 30 km. Inevitably, except for the jitter at the subscriber station at 30 km, the amount of jitter dropped significantly at the other intervals.  
Figure 5: Traffic Delay.  
Figure 6: Jitter.  
Figure 7 presents the traffic that dropped over the network to the three different subscriber stations. The further the subscriber station from the base station, the more traffic the network would drop. However, the network consistently retained the packets, which could be attributed to the wireless MAN-FDMA (orthogonal frequency division multiple access) abilities and the TCP/UDP at the transport layer level. Using TCP/UDP ensures that the network loses or drops packets.  
The simulation results show that metrics balance network deficit with the traffic load to optimize efficiency. For example, the continued simulation showed that the further the subscriber station, the more the jitter was reduced.  
The consistency of the expected performance metric levels from the three subscriber station distance points suggests that the overall performance was better than expected. The study observed a delay over longer distances and added more nodes to boost the signal. In addition, an apparent characteristic of rural areas, namely, a low population density, means the network may optimize efficiency due to low traffic.