用深度强化学习优化无线网络资源分配，显著节能并提升用户公平性。
Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
- 采用PPO算法联合优化发射功率、带宽切片与用户调度。
- 密集场景下能耗降低70%，用户公平性提升超30%。
- 适合关注6G智能网络与绿色通信的研究者参考。
在开放无线接入网（O-RAN）异构网络中，动态资源分配在不同用户负载下面临复杂优化挑战。本文提出一种近实时无线接入网智能控制器（Near-RT RIC）xApp，基于深度强化学习（DRL）联合优化发射功率、带宽切片和用户调度。利用真实网络拓扑，对比了近端策略优化（PPO）与孪生延迟深度确定性策略梯度（TD3）算法与传统启发式方法。结果表明，基于PPO的xApp在密集场景下将网络能耗最多降低70%，相比吞吐量优先基线，用户公平性提升超过30%。这些发现验证了未来6G架构中集中式、能源感知的AI编排的可行性。
原文摘要 · Abstract (English)
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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