## 4 Results
### Performance for Different Fixed Positions  
In the context of 5G, it is also pertinent to analyze situations in which the users/devices remain stationary at fixed positions, since some services have this characteristic (e.g., Fixed Wireless Access, smart cities with sensors or cameras at lamp posts). We carried out an evaluation performance of the DQN-based strategy considering the same scenario explained in Section 4.1 but this time the evaluation UE remained in a fixed position. In order to explore a variety of situations, we studied the performance of the UE for 400 s in 19 \(\times\) 19 = 361 different positions all around the 500 \(\times\) 500 m scenario, selected from a grid of locations in steps of 25m in both horizontal and vertical directions. We compared the results against the optimal and SINR-based strategies. Figure 8 shows the CDF of the instantaneous values of throughput (\(T_{u}\)) and reward for all the considered positions. It is shown that, like in the evaluation with trajectories, the performance for fixed positions is close to the optimum one and clearly outperforms the SINR-based strategy. In fact, when analyzing some particular fixed positions, the DQN-based strategy is able to outperform the reward of the SINR-based one in up to 65%.  
Figure 7: Statistics of connection modes for all moments of evaluation given 100 studied trajectories.  
Aiming to explain more in detail the situations where the DQN-based strategy can come up with more different decisions than the SINR-based approach, we focused on a specific period of time of one of the evaluated fixed positions where the UE was located in the coordinates (X = 300, Y = 350). For this position, the UE experienced SINR values in the 5G NR (C\({}_{2,2}\)) and LTE (C\({}_{1,2}\)) cells equal to 45.7 dB and 33.8 dB respectively. Due to these perceived values, the SINR-based strategy selected the 5G NR cell during the entire period. In contrast, as shown in Figure 9a, the DQN-based strategy considered the current cell loads, and decided to split the traffic among the two cells. In the case of fixed positions, splitting the traffic becomes important because, if the higher-SINR-detected cell is serving other users, at some point it can get overloaded. However, by considering other aspects such as load as the DQN-based strategy does, it is possible to avoid this type of issue while improving the throughput obtained by the UE/device of interest. This effect can be seen clearly in Figure 9b. For the 20-s analyzed period the obtained average throughput with the SINR-based strategy is 15.3 Mb/s while by using our proposed approach for the same period, the average throughput reaches 40.72 Mb/s, which represents a gain of around 166%. This result reinforces the idea about the importance of optimizing the MC configuration of the UEs.  
Considering the results for all the different fixed positions, it was seen that the error of the DQN-based approach with respect to the optimum was, on average, 3.76%, the 5 th percentile was 0.08% and the 95 th percentile was 13.3%. Similarly, considering all the results obtained with the 100 trajectories of Section 4.4 and the fixed positions of Section 4.5, the average error was 3.9% and the distribution provides a 5 th percentile of 0.09% and a  
Figure 8: (**a**) CDF of average reward for the UE of evaluation; (**b**) CDF of throughput of the evaluated UE.  
Figure 9: (**a**) Evolution of \(\beta_{m,n}\) in the analyzed period; (**b**) obtained throughput in the analyzed period.  
95 th percentile of 12.5%. This reflects the close to optimum behavior obtained with the proposed approach.