## 5 Experiments with Frame Aggregations
### Non-Stationary Scenario  
In this subsection, we evaluate the performance of the proposed MAB algorithms in a non-stationary scenario while all wireless nodes operated with the IEEE 802.11 ax standard. For this scenario, we let a sender move away from or toward its receiver at a speed of 6 m/s during the simulation time of 11 s. The initial distance between the sender and receiver was set to 5 m. Thus, when the sender moved away from the fixed receiver, the distance between the two nodes changed from 5 to 71 m. In the opposite direction, the distance started at 71 m and ended at 5 m. The sliding window time \(\tau\) was set to 300 ms considering the simulated network environment. In contrast to the previous experiments, we let all the wireless nodes be equipped with two antennas each. Thus, both the 2 \(\times\) 2 MIMO and 1 \(\times\) 1 SISO modes could be used by the sender and \(K\) was 24 in the experiments. Because of the increased speed of the high MCS levels by the 2 \(\times\) 2 MIMO mode, the sender transmitted CBR traffic of 300 Mbps to the receiver during the simulation time of 11 s.  
Figure 7 shows the changes in the application throughputs (0.1 s average) achieved by the receiver from different MAB algorithms when the sender moved away or toward the receiver. Figure 7a shows the experimental results when the sender moved away from the receiver during the simulation time of 11 s. In Figure 7a, for the better visualization of the results during the time interval in which we are more interested, we also show the changes in the application throughputs during 9 s to 11 s in a separate window. The results in the figure show that TS-logR and KL-UCB-R achieved better performance than their counterparts, particularly when the distance became large. Figure 7b shows the experimental results when the sender moved toward the receiver during the simulation time of 11 s. In Figure 7b, we also show the changes in the application throughputs during 0 s to 2 s in a separate window. Figure 7b clearly shows that TS-logR and KL-UCB-logR achieved better performance than TS-R and KL-UCB-R, respectively, particularly when the distance between the sender and receiver was large. In Figure 8, we show the final application throughputs achieved by the receiver when the sender adopted different transmission rate control algorithms during the 11 s simulation time. For the experiments, we repeated the simulations ten times with different seed values  
Figure 6: (**a**) Changes in application throughputs achieved by different MAB algorithms, (**b**) changes in average MCS levels by different MAB algorithms in 1 \(\times\) 1 IEEE 802.11 ax networks. for randomness. Figure 8 shows their averages and standard deviations. The figure also shows the simulation results of the Ideal and Minstrel-HT rate control algorithm in the ns-3 simulator for the performance comparison purpose. Minstrel-HT is an upgraded version of the Minstrel for high-throughput (HT) wireless network standards such as IEEE 802.11n. The experimental results in Figure 8 show that TS-logR and KL-UCB-logR outperformed their counterparts when the sender moved away from the receiver. The experimental results in Figure 8 also show that TS-logR and KL-UCB-logR outperformed their counterparts when the sender moved toward to the receiver. In addition, TS-logR achieved a higher throughput than KL-UCB-logR and Minstrel-HT in both directions. Overall, the experimental results in Figure 8 show that the proposed MAB algorithms performed better than their counterparts in both directions in this scenario. The performance improvements in both directions from the results in Figure 8 and their averages are summarized in Table 3.  
Figure 8: (a) Average of final application throughputs when the sender moved away from the receiver, (b) average of final application throughputs when the sender moved toward the receiver in \(2\times 2\) IEEE 802.11 ax networks.