Deep Reinforcement Learning Reconfigurable Smart Surface Sensing MAC Protocol for Terahertz Mesh Networks

Terahertz (THz) communication is a key enabler for 6G wireless networks but suffers from severe path loss and dynamic blockage, making conventional MAC protocols inefficient. This paper proposes a reconfigurable intelligent surface–based deep reinforcement learning MAC (RIS‐DRL‐MAC) framework that enables cross‐layer optimisation between the physical and MAC layers. By embedding RIS perception features—such as equivalent channel gain and link stability—into the state space and jointly optimising beam direction, channel access and RIS phase configuration through a distributed twin delayed deep deterministic policy gradient (TD3) algorithm, the protocol achieves adaptive environment control. Simulation results show that, under dynamic blockage and high‐load conditions, RIS‐DRL‐MAC improves network throughput by up to 90%, reduces access delay by 50% and maintains over 90% link availability compared with baseline schemes. The proposed method establishes a closed loop of sensing, decision and environment reconfiguration, providing an effective solution for reliable and energy‐efficient THz mesh networking.