## 5 Conclusions and Future Work  
This paper proposes the use of Deep Q-Network for splitting the traffic of a UE among cells when using multi-connectivity depending on the current traffic and radio conditions experienced by the UE in the involved cells. The proposed strategy was evaluated and compared against the optimum case and against a classical SINR-based approach in different evaluation scenarios, involving UEs following trajectories and stationary UEs. Results show the capability of the DQN agent to learn a quasi-optimal policy, providing a reward that on average is only 3.9% lower than the reward of the optimum strategy. It was also seen that the DQN-based approach clearly outperforms the SINR-based approach with reward differences that can be up to 50% for certain trajectories. In turn, for the case of fixed positions, results show that the DQN-based strategy can achieve throughput gains of up to 166%with respect to the SINR-based strategy at certain times. The statistics regarding the different connection modes used by the DQN strategy confirm the capability  
Figure 10: (**a**) Average reward values as a function of the number of MC-capable UEs; (**b**) average throughput values as a function of the number of MC-capable UEs.  
of the algorithm to optimize the decision on when to use MC or not, to maximize the performance. Moreover, a performance analysis of the DQN-based strategy was conducted when applied to different numbers of MC-capable users coexisting in the same evaluation environment. The results from this study reveal a significant better performance for all the MC-capable users when applying the solution, which reinforces and confirms the relevance of optimizing the MC configuration.  
Overall, our results reflect a promising performance of the proposed DQN-based approach that opens the door for continuing the work around some future research lines. Particularly, the solution could be extended to incorporate other types of traffic with service requirements other than the bit rate considered here. For example, Ultra Reliable and Low Latency Communications (URLLC) services can be considered to exploit MC for enhancing reliability, e.g., by duplicating packets through multiple cells. The exploitation of the proposed mechanisms in other scenarios in which MC can be used with other UEs acting as relays can also be tackled as future work.  
Conceptualization, J.J.H.-C., J.P.-R., O.S., I.V. and F.C.; Data curation, J.J.H.-C., J.P.-R., O.S. and I.V.; Formal analysis, J.J.H.-C., J.P.-R., O.S., I.V. and F.C.; Investigation, J.J.H.-C., J.P.-R., O.S., I.V. and F.C.; Methodology, J.J.H.-C., J.P.-R., O.S., I.V. and F.C.; Software, J.J.H.-C., J.P.-R. and I.V.; Supervision, J.P.-R., O.S. and F.C.; Validation, J.J.H.-C., J.P.-R., O.S., I.V. and F.C.; Writing - original draft, J.J.H.-C.; Writing - review & editing, J.P.-R., O.S., I.V. and F.C. All authors have read and agreed to the published version of the manuscript.  
This paper is part of ARTIST project (ref. PID2020-115104 RB-I00) funded by MCIN/AEI/10.13039/501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691.  
Not applicable.  
The authors declare no conflict of interest.