## 4 Experiments without Frame Aggregations
### Non-Stationary Scenario  
In this subsection, we evaluate the performance of the proposed MAB algorithms in a non-stationary scenario. In this scenario, we let one node (the sender) move away from or toward the other node (the receiver) at a speed of 6 m/s during a simulation time of 10 s. The receiver was located at a fixed position during the simulation time. The initial distance between the sender and the receiver was set to 5 m. As a result, when the sender moved away from the receiver, the distance between the two nodes changed from 5 to 65 m. In the opposite direction, the distance began at 65 m and ended at 5 m. During the simulation time, the sender transmitted CBR traffic of 54 Mbps to the receiver. The sliding window time was set to 100 ms considering the simulated network environment.  
Figure 3 shows the experimental results when the sender adopted different MAB algorithms. Figure 3a shows the changes in the application throughputs (0.1 s average) achieved by the receiver when the sender moved away from the receiver. It shows that TS-logR had a better performance than TS-R, particularly when the distance between the sender and receiver was large. In addition, KL-UCB-logR had a better performance than KL-UCB-R, which exhibited low performance from approximately the middle of the simulation time. The experimental results in Figure 3a imply that the proposed MAB algorithms select low MCS levels more accurately than their counterparts when the SINR is low.  
Figure 3b shows the changes in the application throughputs (0.1 s average) achieved by the receiver when the sender moved toward the receiver. The experimental results in Figure 3b clearly show 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. The experimental results in Figure 3a,b indicate that by adopting the logarithmic values of the transmission rates, the proposed MAB algorithms performed better than their counterparts in the rapidly changing channel environments.  
Figure 3: (**a**) Changes in application throughputs when the sender moved away the receiver, (**b**) changes in application throughputs when the sender moved toward the receiver in IEEE 802.11g networks.  
In Figure 4, we show the final application throughputs achieved by the receiver when the sender adopted different transmission rate control algorithms during the 10 s simulation time. For the experiments, we repeated the simulations ten times with different seed values for randomness. The results in Figure 3(a),b show their averages and standard deviations. The figure also shows the simulation results from two non-MAB-based transmission rate control algorithms in the ns-3 simulator called Minstrel and Ideal for performance comparison purpose.  
Minstrel controls data transmission rates by adjusting the MCS levels of data packets using the results from periodic probing data packets with different MCS levels. Minstrel is one of the most well-known transmission rate control algorithms because it is widely adopted in practical operating systems such as Linux [39]. The Ideal transmission rate control algorithm in the ns-3 simulator assumes that the SINRs of all the arrived packets at the receivers are known to the sender of the packets. Thus, using the SINR information, the Ideal transmission rate control algorithm can achieve almost ideal performance in a given wireless environment.  
The experimental results in Figure 3(a),b clearly show that, by adopting the logarithmic values of the transmission rates, the two proposed MAB algorithms performed better than their counterparts in the rapidly changing channel environments. The results also show that TS-logR performed better than KL-UCB-logR and Minstrel in both directions. Additionally, TS-logR achieved almost as high a performance as the Ideal transmission rate control algorithm in both directions. The overall performance improvements from the results in Figure 4 are summarized in Table 2.