Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning

Abstract

Dynamic resource allocation in open radio access network (O-RAN) heterogeneous networks (HetNets) presents a complex optimisation challenge under varying user loads. We propose a near-real-time RAN intelligent controller (Near-RT RIC) xApp utilising deep reinforcement learning (DRL) to jointly optimise transmit power, bandwidth slicing, and user scheduling. Leveraging real-world network topologies, we benchmark proximal policy optimisation (PPO) and twin delayed deep deterministic policy gradient (TD3) against standard heuristics. Our results demonstrate that the PPO-based xApp achieves a superior trade-off, reducing network energy consumption by up to 70% in dense scenarios and improving user fairness by more than 30% compared to throughput-greedy baselines. These findings validate the feasibility of centralised, energy-aware AI orchestration in future 6G architectures.