{"ID":2863467,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25284","arxiv_id":"2509.25284","title":"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.","short_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,...","url_abs":"https://arxiv.org/abs/2509.25284","url_pdf":"https://arxiv.org/pdf/2509.25284v2","authors":"[\"Oluwaseyi Giwa\",\"Jonathan Shock\",\"Jaco Du Toit\",\"Tobi Awodumila\"]","published":"2025-09-29T09:48:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.NI\",\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}