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arXiv · 2509.25284

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

Oluwaseyi Giwa·Jonathan Shock·Jaco Du Toit·Tobi Awodumila
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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.

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Oluwaseyi Giwa, Jonathan Shock, Jaco Du Toit, Tobi Awodumila. 2025-09-29. Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning. https://doi.org/10.1109/eucnc%2F6gsummit68295.2026.11577494

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