用元强化学习提升O-RAN动态资源分配效率
Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
- 基于MAML思想设计元强化学习框架，实现快速自适应
- 在新网络环境下资源分配性能提升19.8%
- 适合研究O-RAN智能控制与动态优化的开发者
随着无线网络支持更复杂应用，开放无线接入网（O-RAN）架构凭借其智能无线接入网控制器（RIC）模块，成为实时收集、分析网络数据并动态管理无线资源（如资源块和下行功率分配）的关键方案。利用人工智能与机器学习技术，O-RAN以前所未有的效率和灵活性应对现代网络的可变需求。尽管已有基于机器学习的优化策略，但在不可预测环境中的动态资源分配仍面临挑战。本文提出一种受模型无关元学习（MAML）启发的新型元深度强化学习（Meta-DRL）策略，结合O-RAN解耦架构与虚拟分布式单元（DUs），实现自适应、本地化的决策机制，显著提升网络效率。通过引入元学习，系统可快速适应新网络条件，在线优化资源分配，相比传统方法提升网络管理性能19.8%，推动下一代无线网络能力发展。
原文摘要 · Abstract (English)
As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Utilizing artificial intelligence (AI) and machine learning (ML), O-RAN addresses the variable demands of modern networks with unprecedented efficiency and adaptability. Despite progress in using ML-based strategies for network optimization, challenges remain, particularly in the dynamic allocation of resources in unpredictable environments. This paper proposes a novel Meta Deep Reinforcement Learning (Meta-DRL) strategy, inspired by Model-Agnostic Meta-Learning (MAML), to advance resource block and downlink power allocation in O-RAN. Our approach leverages O-RAN's disaggregated architecture with virtual distributed units (DUs) and meta-DRL strategies, enabling adaptive and localized decision-making that significantly enhances network efficiency. By integrating meta-learning, our system quickly adapts to new network conditions, optimizing resource allocation in real-time. This results in a 19.8% improvement in network management performance over traditional methods, advancing the capabilities of next-generation wireless networks.
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