用神经进化提升5G智能控制器的稳定性，不干扰网络运行
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps
- 在近实时控制器中并行部署神经进化优化器，辅助深度强化学习
- 实验表明智能控制器鲁棒性显著提升，计算开销可控
- 适合5G RAN智能化、可靠性要求高的场景
开放无线接入网（O-RAN）架构引入无线接入网智能控制器（RIC），用于管理与优化解耦的无线接入网。强化学习（RL）及其深度形式（DRL）被广泛用于设计智能控制器（xApps），部署于近实时（near-RT）RIC中。然而，这些模型常陷入局部最优，影响其在无线接入网智能控制中的可靠性。为此，本文提出联邦式O-RAN神经进化增强深度强化学习（F-ONRL），在近实时控制器中并行部署基于神经进化（NE）的优化器xApp。该框架在不干扰网络运行的前提下，实现有效探索与利用。我们在Open AI Cellular（OAIC）平台上实现了NE xApp与DRL xApp的联合部署，数值结果表明，xApps的鲁棒性显著提升，同时有效平衡了额外计算负载。
原文摘要 · Abstract (English)
The open radio access network (O-RAN) architecture introduces RAN intelligent controllers (RICs) to facilitate the management and optimization of the disaggregated RAN. Reinforcement learning (RL) and its advanced form, deep RL (DRL), are increasingly employed for designing intelligent controllers, or xApps, to be deployed in the near-real time (near-RT) RIC. These models often encounter local optima, which raise concerns about their reliability for RAN intelligent control. We therefore introduce Federated O-RAN enabled Neuroevolution (NE)-enhanced DRL (F-ONRL) that deploys an NE-based optimizer xApp in parallel to the RAN controller xApps. This NE-DRL xApp framework enables effective exploration and exploitation in the near-RT RIC without disrupting RAN operations. We implement the NE xApp along with a DRL xApp and deploy them on Open AI Cellular (OAIC) platform and present numerical results that demonstrate the improved robustness of xApps while effectively balancing the additional computational load.
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