Alternating Deep Reinforcement Learning for Wireless Resource Allocation in Cell-Free Systems

With the rapid development of 5 G, managing resources to achieve high throughput and low latency has become an increasingly prominent challenge. In this paper, we propose an alternating deep reinforcement learning (ADRL) framework for dynamic resource allocation in cell-free systems, aiming to enhance the sum-rate and reduce delay violation probability by optimizing the association between access points (APs) and user equipments (UEs), along with subcarrier allocation, beamforming, and power control. To achieve efficient resource management while simplifying decision-making, the ADRL framework employs diverse deep reinforcement learning (DRL) networks to alternately optimize specific resources at different time granularities. Simulation results demonstrate improvements in both throughput and delay performance, highlighting the potential of the proposed framework in advancing industrial wireless communication systems.