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.

翻译：开放无线接入网(O-RAN)异构网络(HetNets)中的动态资源分配在用户负载变化条件下呈现复杂的优化挑战。我们提出一种利用深度强化学习(DRL)的近实时RAN智能控制器(Near-RT RIC) xApp，用于联合优化发射功率、带宽切片和用户调度。借助真实网络拓扑，我们将近端策略优化(PPO)和双延迟深度确定性策略梯度(TD3)算法与标准启发式方法进行基准对比。结果表明，基于PPO的xApp实现了更优的权衡：在密集场景下网络能耗降低高达70%，且与吞吐量贪婪基线相比，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、能量感知型人工智能编排的可行性。