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.

翻译：随着开放无线接入网（O-RAN）与5G等新兴网络的持续发展，具有不同需求的各种服务需求日益增长。网络切片作为应对差异化服务需求的潜在解决方案应运而生，但在动态环境中管理网络切片并维持服务质量（QoS）仍是一项挑战。利用机器学习方法对动态网络进行最优控制，可通过预防服务等级协议（SLA）违规来提升网络性能，这对实现可靠决策及满足新兴网络需求至关重要。尽管基于强化学习（RL）的控制方法能有效实现网络QoS的实时监控与调控，但提升决策可靠性仍需泛化能力。本文提出一种创新的基于注意力的深度强化学习（ADRL）技术，通过利用O-RAN解耦模块与分布式智能体协作，实现高效信息提取与泛化能力，从而提升网络性能。该方法在分布式智能体之间引入价值注意力网络（Value-Attention Network），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基线方法相比，所提方案在网络性能方面具有显著提升。