# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 6 Performance Evaluation of MAB-ADC  
We evaluate the impact of our proposed coexistence framework in two sets of simulations. The first set comprises a short simulation to assess the framework's ability to switch between different DC values in response to a change in the environment. The second set is a longer simulation that considers different offered data rates. 1. It has 40 s of duration, and the offered data rate changes after 20 s;
2. The offered data rate is initially 4 Mbps for LTE-U users and 500 kbps for Wi-Fi users. At 20 s, the data rates are inverted. This approach comprises two different unbalanced scenarios regarding the system's demand. When LTE-U users have a much higher offered data rate, the optimal DC value is the one that gives more channel time to LTE-U, that is, a DC value closer to 1. However, if Wi-Fi users have a higher offered data rate, the optimal DC value is the one that gives more channel time to Wi-Fi, closer to 0;
3. The algorithms always operate at the end of the current ABS time (40 ms windows). At that time, they select the DC value for the next ABS interval;
4. For comparison, we use the Q-Learning framework proposed in [10]. It has the same centralized and coordinated operation as one of our proposed MAB operation modes. The QL is defined with four actions \(\{0.2,0.4,0.6,0.8\}\) and four states. Since QL also requires the state's definition, we established only four actions. If the number of actions were to be increased, the convergence time would be affected. For easier comparison, we have included the results of our previous Q-Learning solution in the same figures as the new proposed framework. Whenever we mention QL results, they belong to our previous work. Figure 2 shows a bar graph of the operator's throughput as a function of the fixed Duty Cycle, the previous Q-Learning solution, and the proposed solutions, namely the independent MAB and the coordinated MAB. Comparing the aggregated throughput with the best fixed DC value of 0.5, the MAB solutions present a gain of 9 Mbps (15% gain) and 12 Mbps (20% gain) for the independent and coordinated modes, respectively. However, Q-Learning provides the highest gain, nearly 15 Mbps, which corresponds to a 25% improvement. Both coordinated solutions, MAB and QL, were able to achieve maximum system throughput. However, the LTE operator suffers a 20% loss, with the Wi-Fi operator showing a significantly higher throughput compared with LTE. The independent MAB, on the other hand, was able to provide positive gain for both operators, while still showing a substantial  
Figure 2: Simulation 1: Operator’s throughput vs. fixed DC and proposed solutions. gain in system throughput. It improves the LTE by over 30% and Wi-Fi by approximately 2%, providing similar throughput for both operators. This behavior may be caused by the different degrees of freedom. In the coordinated solution, the base stations must use the same DC value determined by the proposed framework. On the other hand, in the independent solution, each base station has its own DC value, providing a higher degree of freedom and thus allowing different combinations of DC values. The histogram in Figure 3 shows that during the simulation for coordinated MAB, the most frequently selected DC values were 0.3 and 0.7. This observation aligns well with the expected behavior, since a simulation that is divided into two very different parts should also present two optimal values. For the first 20 s, the offered data rate is 4 Mbps for LTE and 500 kbps for Wi-Fi. Given this configuration, the best DC value in terms of throughput is expected to favor LTE when allocating transmission time. Consequently, the optimal value found by the coordinated MAB was 0.7, which gives 30% of the time window for Wi-Fi and 70% for LTE. From 20 s on, the offered data rates are inverted. Hence, the best DC value in terms of throughput should now favor Wi-Fi when allocating transmission time. For that case, the algorithm selected 0.3, which gives 70% of the window time to Wi-Fi and 30% to LTE.