基于注意力机制的深度强化学习开放RAN切片管理
Attention-based Open RAN Slice Management using Deep Reinforcement Learning
AI总结：
针对O-RAN等新兴网络动态环境下切片管理难保障QoS的问题，提出带价值注意力网络的分布式ADRL方法，借助O-RAN解耦模块与智能体协作提升泛化性，仿真显示性能优于现有DRL基准。
摘要：
随着开放无线接入网（O-RAN）、5G等新兴网络持续发展，具有不同需求的各类服务的需求不断增长。网络切片已成为应对不同服务需求的潜在解决方案。然而，在动态环境中管理网络切片同时维持服务质量（QoS）是一项具有挑战性的任务。利用机器学习（ML）方法对动态网络进行最优控制，可通过避免违反服务水平协议（SLA）来提升网络性能，这对于可靠决策和满足新兴网络需求至关重要。尽管基于强化学习（RL）的控制方法在实时监控与调控网络QoS方面效果显著，但提升泛化能力对于增强决策可靠性十分必要。本文提出一种创新的基于注意力的深度RL（ADRL）技术，该技术借助O-RAN的解耦模块与分布式智能体协作，通过高效信息提取与泛化实现更优性能。所提方法在分布式智能体之间引入价值注意力网络，以实现可靠且最优的决策。仿真结果表明，与其他深度强化学习（DRL）基准方法相比，该方法在网络性能上有显著提升。
英文摘要：
As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
