RIS-Empowered MEC for URLLC Systems with Digital-Twin-Driven Architecture

This paper investigates a digital twin (DT) enabled reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (e2e) latency minimization for the considered system under the joint optimization of beamforming design at RIS, power and bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP), and later we adopt a deep reinforcement learning (DRL) algorithm to solve it effectively. Simulation results confirm that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network achieves up to 60% lower transmission delay and 20% lower energy consumption compared to without RIS scheme.