Sixth-generation (6G) networks leverage simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) to overcome the limitations of traditional RISs. STAR-RISs offer 360-degree full-space coverage and optimized transmission and reflection for enhanced network performance and dynamic control of the indoor propagation environment. However, deploying STAR-RISs indoors presents challenges in interference mitigation, power consumption, and real-time configuration. In this work, a novel network architecture utilizing multiple access points (APs) and STAR-RISs is proposed for indoor communication. An optimization problem encompassing user assignment, access point beamforming, and STAR-RIS phase control for reflection and transmission is formulated. The inherent complexity of the formulated problem necessitates a decomposition approach for an efficient solution. This involves tackling different sub-problems with specialized techniques: a many-to-one matching algorithm is employed to assign users to appropriate access points, optimizing resource allocation. To facilitate efficient resource management, access points are grouped using a correlation-based K-means clustering algorithm. Multi-agent deep reinforcement learning (MADRL) is leveraged to optimize the control of the STAR-RIS. Within the proposed MADRL framework, a novel approach is introduced where each decision variable acts as an independent agent, enabling collaborative learning and decision-making. Additionally, the proposed MADRL approach incorporates convex approximation (CA). This technique utilizes suboptimal solutions from successive convex approximation (SCA) to accelerate policy learning for the agents, thereby leading to faster environment adaptation and convergence. Simulations demonstrate significant network utility improvements compared to baseline approaches.

翻译：第六代（6G）网络利用同时透射与反射可重构智能表面（STAR-RIS）来克服传统RIS的局限性。STAR-RIS提供360度全空间覆盖，并通过优化的透射与反射来增强网络性能，实现对室内传播环境的动态调控。然而，在室内部署STAR-RIS面临着干扰抑制、功耗和实时配置等方面的挑战。本文提出了一种利用多接入点（AP）与多STAR-RIS的新型室内通信网络架构。构建了一个涵盖用户关联、接入点波束成形以及STAR-RIS反射与透射相位控制的优化问题。该问题固有的复杂性要求采用分解策略以获取高效解。这涉及运用专门技术处理不同子问题：采用多对一匹配算法为用户分配合适的接入点，以优化资源分配；为促进高效的资源管理，使用基于相关性的K-means聚类算法对接入点进行分组。利用多智能体深度强化学习（MADRL）来优化STAR-RIS的控制。在所提出的MADRL框架中，引入了一种新颖的方法，其中每个决策变量作为一个独立的智能体，实现协同学习与决策。此外，所提出的MADRL方法融合了凸近似（CA）技术。该技术利用逐次凸近似（SCA）获得的次优解来加速智能体的策略学习，从而实现更快的环境适应与收敛。仿真结果表明，与基线方法相比，所提方案能显著提升网络效用。