## 4 Results
### Performance Evaluation of the DQN-Based Strategy  
#### 4.4.1 Performance for Different Trajectories  
In order to assess the benefits brought by the proposed DQN-based MC strategy, this subsection compares the performance obtained by the proposed approach against the two reference strategies mentioned before, the _optimum strategy_, and the _SINR-based strategy_. To conduct this evaluation, the DQN-based MC strategy makes use of the policy learnt after completing the training process explained in Section 4.2. This policy is applied to the simulation of a UE of interest following 100 different straight trajectories of duration 400 s with different starting points and directions covering the entire evaluation scenario area. In each time window, the MC configuration according to the policy is applied and the resulting reward and throughput is measured. The same evaluation is executed when applying the two other reference strategies with the same trajectories. Figure 4 shows the average reward obtained for each one of the trajectories with all the considered strategies. It is observed that the DQN-based strategy achieves a performance very close to the optimum one in all the studied cases, with differences less than 1% in some of the trajectories, which confirms the good behavior of the proposed approach. In  
Figure 3: Evolution of the average reward as a function of the training steps. turn, if we analyze the reward of the 100 trajectories and obtain the average for all of them, the DQN-based approach outperforms the SINR-based approach in around 13.1% but the improvement can be as big as 50% in certain trajectories (e.g., trajectory 95). This main advantage of the DQN-based strategy with respect to the classical SINR-based strategy is due to smartly splitting the traffic of the UE among the cells to avoid overload and enhance the obtained bit rate. From an implementation perspective, while the SINR-based approach can be implemented relying on handover procedures, the DQN approach requires the support of the MC feature and that the base stations handling the involved cells are interconnected through the Xn interface in case of 5G NR or the X2 interface in case of LTE. Both requirements are now commonplace in live 4G/5G networks, which rely on the MC feature for supporting the widely used 5G NSA operation. Regarding comparison with the optimum strategy, the error obtained with the DQN-based approach was computed as the percentage of difference between the reward of both strategies. The average of this error for all the trajectories was 4.32%. Similarly, the 5 th percentile of the error was 0.35% and the 95 th percentile was 7.94%. This reflects that the DQN-based approach achieves a performance quite close to the optimum but without having to exhaustively search for all the possible MC configurations. In order to assess the throughput performance, Figure 5 shows plots for each strategy of the cumulative distribution function (CDF) of the instantaneous throughput (\(T_{u}\)) values obtained by the UE of interest with all the considered trajectories. Again, it is observed that the DQN-based strategy achieves a close performance to the optimum strategy and clearly outperforms the SINR-based strategy. In particular, with the DQN-based approach the UE throughput achieved 90% of the time is higher or equal to 37 Mb/s, while with the SINR-based approach this value is only 18 Mb/s. Similarly, the DQN-based approach is able to provide the throughput of 50 Mb/s during around 50% of the time, while the SINR-based approach only provides it 35% of the time. Aiming to further assess the behavior of the DQN-based strategy, we carried out a more detailed analysis focusing on a specific trajectory of a given UE starting in the point [X\({}_{1}\) = 485, Y\({}_{1}\) = 181] and following a straight trajectory at 1 m/s up to the point [X\({}_{2}\) = 85, Y\({}_{2}\) = 181]. For this specific trajectory, the SINR-based average reward was 0.634 while for the DQN approach this average reward rose to 0.835 which represents a gain of around 31%.