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.

通过多个关键接入点（APs）和自适应重构智能表面（STAR-RISs）的新型网络架构，该研究论文提出了应对室内通信中干扰抑制、功耗和实时配置方面的挑战的解决方案，并采用多智能体深度强化学习（MADRL）来优化 STAR-RIS 的控制。通过模拟实验，论文证明了与基准方法相比，网络效用显著提高。