  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.
