Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning

Abstract

Self-healing radio access network (RAN) requires autonomous detection and compensation of base station (BS) failures. This letter proposes an end-to-end framework combining three-class cell outage detection (COD), distinguishing normal, failed, and collaterally degraded cells, with a deep Q-Network (DQN) based deep reinforcement learning (DRL) agent that jointly controls power and antenna tilt for cell outage compensation (COC). Evaluation results show that the proposed DQN agent achieves 99.1% coverage and 54% full-recovery rate, an 11$\times$ improvement over the best heuristic, while consuming less compensation energy than heuristic baselines and learning, without explicit geometric input, to prefer tilt-only compensation for centre-cell outage.