AI RESEARCH
Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
arXiv CS.LG
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ArXi:2509.25284v2 Announce Type: replace 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.