# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results
### Case D  
In this scenario, four users are mobile while eight users are stationary. The average BER values, shown in Figure 13a,b, illustrate that the RL-CAA is consistent in providing a \(\sim\)10% error rate while SCA is giving slightly more than 10%. Since few nodes are mobile, less handovers are occurring, therefore throughputs observed at each gateway are more consistent, as shown in Figure 14a,b. The RL-CAA provides a better overall throughput, e.g., at G2 and G3 the average throughput is over 3 Mbps. The individual dissatisfaction probability for the RL-CAA is almost zero (Figure 15b). The SCA provides poor QoS and the nodes are dissatisfied. The total average system dissatisfaction probabilities of the RL-CAA and the SCA are compared in Figure 15c. The overall dissatisfaction for the RL-CAA is negligible. Figure 12: (**a**) Individual nodes dissatisfaction probabilities for the SCA algorithm; (**b**) Individual nodes dissatisfaction probabilities for the RL-CAA algorithm; (**c**) Comparison of the RL-CAA and SCA based total dissatisfaction probabilities averaged over time. As previously discussed, the RL-CAA is a computationally expensive algorithm. We managed to minimize its execution time in a traffic-changing environment by introducing a throughput margin for the acceptable throughput. The lower the throughput margin, the higher the number of executions of the RL-CAA in a given amount of time. There is a tradeoff between QoS requirements and the acceptable throughput margin. The results presented above correspond to a 5% margin for the throughput. Figure 14: (**a**) Average throughput for the SCA algorithm; (**b**) Average throughput for the RL-CAA algorithm. Figure 13: (**a**) Average BER for the SCA algorithm; (**b**) Average BER for the RL-CAA algorithm. Based on these simulated results, the proposed RL-CAA gives an overall better performance. In terms of BER, the RL-CAA performs slightly better than the SCA; we can summarize that the RL-CAA and the SCA exhibits similar behaviors. However, the RL-CAA gives better performance in terms of throughput and dissatisfaction probabilities, which is the main aim in this work. We analyze the results of the RL-CAA with different throughput margins. Case B is the most dynamic with the highest mobility, handovers and changing traffic, hence it is the most challenging case for the algorithm in terms of complexity, dynamism and robustness. The RL-CAA is analyzed only for the QoS satisfaction for the case B, for 5%, 10%, 25% and 50% throughput margin. It is evident from Figure 16a that the 5% margin gives the best individual nodes' QoS satisfaction, i.e., the individual dissatisfaction probability is lowest. Figure 15: (**a**) Individual nodes dissatisfaction probabilities for the SCA algorithm; (**b**) Individual nodes dissatisfaction probabilities for the RL-CAA algorithm; (**c**) Comparison of the RL-CAA and SCA based total dissatisfaction probabilities averaged over time. This aspect can also be presented in the form of the total averaged dissatisfaction probability of all the margin values, as plotted in Figure 16b. The 5% margin shows the lowest dissatisfaction probability among all. The 50% throughput margin yields almost the same dissatisfaction probability as the SCA (refer to Figures 8 and 9). The results show that for the presented experiments, the RL-CAA yields the best results for channel allocation in a highly dynamic environment and that a 5% margin is good tradeoff to achieve the benefits of unsupervised learning and making the WBAN as C-BAN. To evaluate our power optimization algorithm, we take the example of Zone 1 with gateway G1. We use three reference users located at A, B and C (Figure 2). These users are located at the edges of their zones, which is also the edge of Zone 1. This is the closest point where these users can come into the coverage radius of G1. The available transmission power ranges from \(-13\) to \(3\) dB (Table 1).