Scalable Reinforcement Learning-Based Environment-Aware Scheduler for Enhancing Reliability and Availability in 6G Networks

Abstract

The sixth generation (6G) of wireless networks must ensure high reliability, availability, and fairness, even in dynamic and challenging environments. Environmental-aware knowledge, such as that obtained from Radio Environment Maps (REMs), offers predictive insights into channel conditions and can guide more effective scheduling decisions. This paper proposes a scalable deep reinforcement learning (DRL) framework that exploits such knowledge to optimize multi-user scheduling under limited resources, in order to enhance reliability and availability while maintaining fairness. Unlike standard deep Q-network (DQN), which evaluates Q-values per action, we propose a novel learning method that estimates per-user Q-values, enabling user selection with per-decision complexity that scales linearly with the number of users. Simulation results show that the proposed approach consistently balances reliability and availability while maintaining fairness, outperforming Round Robin (RR) and Proportional Fair (PF) schedulers, especially in environments with high clutter density and frequent non-line-of-sight conditions. In particular, the proposed method improves reliability by over 400% with only a 2% drop in availability, compared to the RR scheduler. Relative to PF, it improves availability and fairness by 23% and 40%, respectively, without sacrificing reliability. These improvements are observed under balanced propagation conditions, where the probabilities of line-of-sight and non-line-of-sight are equal due to a clutter density of 50%. These results highlight the potential of the proposed environmental-aware DRL scheduler to support trustworthy 6G communication in complex and dynamic environments.