Towards Optimal Path Allocation for Unreliable Reconfigurable Intelligent Surfaces

TL;DR

This paper develops a path allocation model for THz communication networks with reconfigurable intelligent surfaces, addressing RIS hardware failures to improve VR service reliability using a Semi-Markov decision process.

Contribution

It introduces a novel SMDP-based path allocation framework that accounts for RIS failures, optimizing long-term system reward in unreliable THz RIS-enabled networks.

Findings

The model improves VR service reliability under RIS failure scenarios.

Optimal path allocation reduces VR blocking probability.

System performance varies with RIS failure rates and service demand.

Abstract

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 path allocation solution becomes a necessity. Assuming the RIS component is the most critical one in enabling the service, we investigate the impact of RIS hardware failure on path allocation performance. To this end, we study a THz network that employs THz operated RISs acting as base stations, serving VR users. We propose a Semi-Markov decision Process (SMDP)-based path allocation model to ensure the reliability of THz connection, while maximizing the total long-term expected system reward, considering the system gains, costs of link 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 path 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.