# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
## 6 Performance Evaluation
### Uplink Transmission in Unstable Channel Environment  
Figures 7 and 8 show the network throughput performance when uplink channels are assumed to be unstable and MCSs of 80% and 40% of stations change every simulation time, respectively. For the proposed method with a re-clustering strategy, \(\lambda\) is set to 1.5, and the methods perform re-clustering if the condition in (6) is satisfied. The simulation results show that the network throughput performance decreases as the channel becomes unstable, i.e., more stations experience MCS change owing to the time-varying channels between  
Figure 6: Network throughput performance (\(S=100\)).  
stations and the AP. For the same values of \(\alpha\) and \(\beta\), the proposed method with re-clustering shows better throughput performance than the proposed method without re-clustering. This implies that performing re-clustering is important for enhancing network throughput performance in a time-varying channel environment. Moreover, for the uplink transmission scenarios in Figure 7, the proposed method with re-clustering (\(\alpha=0.6\) and \(\beta=0.4\)) shows similar throughput performance to the proposed method without re-clustering (\(\alpha=0.8\) and \(\beta=0.2\)).  
Figures 9 and 10 shows the fairness performance of the proposed method in an unstable channel environment. As in the results in stable uplink scenarios, fairness performance increases as \(\alpha\) decreases and \(\beta\) increases. On the other hand, the simulation results show similar fairness performance regardless of the channel environment. By performing station re-clustering based on transmission delay in an unstable channel environment, throughput performance can be increased while maintaining fairness performance.  
Figure 8: Network throughput performance (MCS of 40% stations changes, \(\lambda=1.5\)).  
Figure 7: Network throughput performance (MCS of 80% stations changes, \(\lambda=1.5\)).  
Figures 11 and 12 show the network throughput performance in an unstable channel when MCSs of 80% and 40% of stations change every simulation time, respectively. Unlike the performance evaluations in Figures 7 and 8, \(\lambda\) for performing re-clustering as in (6) is set to 1.9 instead of 1.5. As shown in the results, with an increased value of \(\lambda\), the proposed method with re-clustering shows similar performance with the proposed method without re-clustering method. In Figure 11, the performance of the proposed method with re-clustering approaches that of the proposed method without re-clustering compared to the results in Figure 12 This implies that \(\lambda\) should be adjusted to a smaller value as channels between stations and an AP becomes unstable.  
Figure 10: Fairness performance (MCS of 40% stations changes, \(\lambda=1.5\)).  
Figure 9: Fairness performance (MCS of 80% stations changes, \(\lambda=1.5\)).  
Through the various simulations, we found that the proposed method can improve the network throughput performance in dense networks with a large number of stations. Note that the proposed method has relatively higher complexity than the other methods because the proposed method has to sort stations based on their transmission delays to create clusters, and cluster-based proportional fair scheduling is performed, while other methods do not need sorting for clustering. However, with the SDR-based experiments described in the following section, we demonstrate the feasibility of the proposed method in real-world scenarios.