# Robust Wireless Local Area Networks Based on Compressed Sensing
## 4 Simulation Results  
In the simulation methodology, the following conditions are used: (1) a set of features or parameters of packets to characterize each class, where these features should be relevant to the suitable classes; (2) The set of known objects for the training dictionary to learn how to classify different packets; (3) In the training phase, the training set is used to decide how the parameters ought to be weighted and combined in order to separate the various classes of packets; (4) In the application phase, the weights determined in the training set are applied to a set of packet objects that do not have known classes in order to determine what their classes are likely to be in any wireless path or wireless device. To evaluate the performance of our methods, the following dataset has been used:  
* Dataset was obtained through the following experiments in three WLANs.
* The experiment participants used various types of wireless networking devices in three WLANs, each was capable of measuring QoS and accessing internet sites from various locations.
* For each access, the latency of WLAN was measured and collected as the end-to-end QoS.
* At the same time, the attribute information of the WLAN was also collected.
* The QoS of WLANs was measured using the speed test application. The proposed architecture allows reducing DDP to 15%, BER to 14% at each wireless node, FDR to 25%, and PD to 15%, which are good records for WLANs. The proposed architecture increased DT to 22% and S/N ratio to 17%, and 10% accuracy of wireless transmission.  
Figure 3 shows data (packet) delay probability versus maximum packet size. It can be observed from Figure 2 that the proposed algorithms exhibit excellent performance in data (packet) delay probability (in our work to 15%). Figure 4 plots the data throughput versus of the maximum packet size. It can be noted that the good level data throughput is obtained by increasing the of the maximum packet size (in our work to 22%).  
Figure 5 shows the S/N versus the number of users. It can be seen from Figure 4 that S/N ratio can be increased to 17% without sacrificing of the performance in the WLAN. Figure 5 plots the normalized bit error rate versus number of packets. It can be seen from Figure 6 that bit error rate can be minimized to 15% in our work. Figure 7 shows accuracy versus of the number of clusters. It can be observed from Figure 2 that the proposed algorithms exhibit excellent performance in accuracy (in our work to 10%). Figure 8 plots the FDR versus of the number of clusters. It can be noted that the  
good level data FDR is obtained by increasing the of the maximum packet size (in our work to 25%). Figure 9 shows the probability versus of packet delay. It can be seen from Figure 4 that packet delay can be decreased to 15% without sacrificing of the performance in the WLAN.  
Figure 4: Data throughput versus of the maximum packet size.  
Figure 3: Data (packet) delay probability versus maximum packet size.  
Figure 5: Signal to Noise (S/N) vs the number of users.  
Figure 6: Normalized bite error rate vs number of packets.  
Figure 7: Accuracy versus of the number of clusters.