# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 6 Performance Evaluation of MAB-ADC
It is also important to note that the DC value of 0.7 is selected fewer times than 0.3, even though the simulation is evenly split into two parts. This behavior is due to the simulation software used. The ns-3 simulator allocates an initial time to configure the servers and start the services. As a result, in the first part of our simulation, when 0.7 is the optimal DC value, the proposed framework has less time to act due to this warm-up period. However, this behavior does not impact the performance, as the results demonstrate that the algorithms effectively select the optimal DC value in each half of the simulation. The second set of simulations complements our conclusions about the proposed solutions in a more complex scenario. The data rate change is more dynamic and the simulation is longer. This new scenario is defined as follows:  
1. The simulation has a duration of 250 s, and the offered data rate changes at specific timestamps throughout the simulation;  
Figure 3: Histogram of the chosen DC value for the first set of simulations of coordinated MAB. 2. The offered data rate is uniformly sampled from the set \(T_{oferted}=\{\) 500 kbps, 1 Mbps, 2 Mbps, 4 Mbps};
3. The specific timestamps where the offered data rate changes occur are uniformly sampled from an interval between 10 and 15 s. These timestamps are added to the current simulation time until it reaches the 250 s limit. As a result, during the 250 s simulation, there will be from 16 to 25 changes in the offered data rate (calculated by dividing the total duration by the minimum and maximum intervals);
4. 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;
5. To ensure statistical confidence, the 250 s simulation was repeated 100 times. In each simulation run (snapshot), the stations are uniformly distributed across the multicell scenario, resulting in different interference profiles, since the stations are in different positions at each snapshot;
6. For comparison, we also use the Q-Learning framework defined in [10]. It has the same centralized and coordinated operations as one of our proposed MAB operation modes. Figure 4 presents a bar graph of the operator's throughput for the second set of simulations. The set of fixed DC values is compared with the proposed solutions and the reference Q-Learning approach. The independent MAB has the highest gain, outperforming the best fixed DC value of 0.6 by approximately 3.5 Mbps. However, the gain achieved by the coordinated MAB and Q-Learning is almost the same as the gain shown in the independent MAB. Regarding the operators, their throughput is more balanced with the independent MAB and the Q-Learning solution and more unbalanced with the coordinated MAB. Additionally, compared with the first set of simulations, all solutions achieve smaller gain. This strongly indicates a dependent relationship between the gain and the difference in the offered data rates. When the offered data rates are more unbalanced, as in the first set of simulations, the solutions present higher gain. On the other hand, if the offered data rates are more equalized, as in the second set of simulations with randomly selected data rates, the gain is smaller. Despite the solutions being designed and modeled to maximize system throughput, they also lead to better user throughput. Figure 5 shows how each solution performs compared with the best fixed DC value of 0.6, specifically in terms of the 10 th percentile. The coordinated MAB, although it provides gain for Wi-Fi users, does come at the cost of a noticeable loss for LTE-U users. The same behavior is shown for the Q-Learning approach, but the ratio loss/gain is more balanced. In contrast, the independent MAB demonstrates a  
Figure 4: Operator’s throughput for the second analyzed simulation. gain of more than 100% compared with the fixed DC value of 0.6, with only a loss of less than 10% for Wi-Fi users.