mlsanityData to 14 Oct 2026

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

arXiv 2509.25284 ↗First posted 29 Sep 2025cs.LG cs.NI eess.SPcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 21 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

6 of 21 references.

References · 6

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2021Convex optimizationcited by 2,515
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2024A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awarenesscited by 2
  6. 2025The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Networkcited by 2

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  1. 2026EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN
  2. 2025Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
  3. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  4. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
  5. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  6. 2026Scalable machine learning-based approaches for energy saving in densely deployed Open RAN ↗
  7. 2025Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty
  8. 2025QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning