2026-05-30T18:02:22Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/385652026-02-03T11:13:47Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Martin, Adrian author De la Bandera Cascales, Isabel author Mendo, Adriano author Outes, Jose author Ramiro, Juan author Barco-Moreno, Raquel author 2025-05-07 5G New Radio (NR) network deployment in Non-Stand Alone (NSA) mode means that 5G networks rely on the control plane of existing Long Term Evolution (LTE) modules for control functions, while 5G modules are only dedicated to the user plane tasks, which could also be carried out by LTE modules simultaneously. The first deployments of 5G networks are essentially using this technology. These deployments enable what is known as E-UTRAN NR Dual Connectivity (ENDC), where a user establish a 5G connection simultaneously with a pre-existing LTE connection to boost their data rate. In this paper, a single Federated Deep Reinforcement Learning (FDRL) agent for the optimization of the event that triggers the dual connectivity between LTE and 5G is proposed. First, single Deep Reinforcement Learning (DRL) agents are trained in isolated cells. Later, these agents are merged into a unique global agent capable of optimizing the whole network with Federated Learning (FL). This scheme of training single agents and merging them also makes feasible the use of dynamic simulators for this type of learning algorithm and parameters related to mobility, by drastically reducing the number of possible combinations resulting in fewer simulations. The simulation results show that the final agent is capable of achieving a tradeoff between dropped calls and the user throughput to achieve global optimum without the need for interacting with all the cells for training. A. Martin et al., "Federated Deep Reinforcement Learning for ENDC Optimization" in IEEE Transactions on Mobile Computing, vol. 24, no. 06, pp. 5525-5535, June 2025, doi: 10.1109/TMC.2025.3534661. https://hdl.handle.net/10630/38565 10.1109/TMC.2025.3534661 Telecomunicaciones Aprendizaje automático (Inteligencia artificial) Federated deep reinforcement Learning for ENDC optimization