A Semi-Markov Decision Process (SMDP)-based Channel Allocation Model for Unreliable Terahertz (THz) Reconfigurable Intelligent Surfaces (RIS)
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Terahertz (THz) communications and reconfigurable intelligent surfaces (RISs) have been recently proposed to enable various powerful indoor applications, such as wireless virtual reality (VR). For an efficient servicing of VR users, an efficient THz channel allocation solution becomes a necessity. Assuming that RIS component is the most critical one in enabling the service, we investigate the impact of RIS hardware failure on channel allocation performance. To this end, we study a THz network that employs THz operated RISs acting as base stations, servicing VR users. We propose a Semi-Markov decision Process (SMDP)-based channel allocation model to ensure the reliability of THz connection, while maximizing the total long-term expected system reward, considering the system gains, costs of channel utilization, and the penalty of RIS failure. The SMDP-based model of the RIS system is formulated by defining the state space, action space, reward model, and transition probability distribution. We propose an optimal iterative algorithm for channel allocation that decides the next action at each system state. The results show the average reward and VR service blocking probability under different scenarios and with various VR service arrivals and RIS failure rates, as first step towards feasible VR services over unreliable THz RIS.
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