# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 4 Experimental Evaluation
### Performance Evaluation  
In this section we present the main results related to the performance evaluation of the proposed technique, CB-AIIM. We have compared our proposal with four other techniques. First, we have considered the simplest channel assignment technique, which consists of choosing a random channel for each AP. We have called this technique random, and although we expect it to have a low performance, it is interesting to include in the evaluation as a baseline. In addition to random, we have also considered other proposals that have been proposed in the literature in the last years and that have been already cited in Section 1. These techniques have been named according to the names of their authors, so KCKC is the technique proposed in [7] and HZNA the one proposed in [26]. Finally, we have also included AIIM [27] to show how the effect of STAs included in our proposal (CB-AIIM) is able to improve the results. Figures 6-9 show the results of the performance comparison of the abovementioned techniques. In these figures we show the global utility (see Section 2.3) of the channel assignment obtained by the different techniques. To obtain the results, all the experiments have been run 20 times. For each technique and in each of the eight settings, we show the average utility obtained, along with the 95% confidence intervals (in black) and the utility of the best and the worst execution (maximum  
Figure 5: Graph representation of the scenario under study for different classroom occupancies. and minimum utility, shown as blue and red crosses, respectively). As we can notice by inspecting Figures 6-9, there are not significant differences in the conclusions that can be obtained depending on the classroom occupancy. As expected, the utility when there are more wireless devices in the network is higher (when the classroom occupancy increases), but the relative performances of the different techniques do not depend on the density of the Wi-Fi network. If we analyze the differences in the performance among the studied techniques, we notice that \"random\" offers the worst performance, as expected. Next, the worst performance was obtained by KCKC, which offers better performance than \"random\" in mean but sometimes gets very poor solutions (see the minimum values for KCKC). However, it is interesting to note that in a few cases KCKC was able also to obtain very good results (see the maximum values for KCKC). From that and especially due to the fact that KCKC usually has the largest confidence intervals, we can conclude that KCKC is the technique with the highest variability from all the studied ones. The performance of AIIM was slightly better than that of KCKC, as it was similar in most scenarios but it was better in both the medium classroom occupancy settings. Moreover, AIIM had lower variability than KCKC. HZNA usually offered greater utility higher than KCKC and AIIM, but not always, as its performance was, in some cases, similar to those of the others. However, from KCKC, HZNA and AIIM, HZNA is the technique with the lowest variability, as its confidence intervals are clearly the smallest. Figure 6: (a,b) Results for two different settings with low classroom occupancy (12 classrooms under use). Figure 7: (a,b) Results for two different settings with medium classroom occupancy (24 classrooms under use). Once the differences in performance of the proposals random, KKCC, HZNA and AIIM were analyzed, it was time to compare our proposal with all of them. Our proposal, CB-AIIM, was able to obtain the best performance in all the studied cases, and it is also interesting to highlight that the varialbility of the results is similar to that obtained by HZNA.