Active-RIS-Assisted Digital Twin-Based URLLC Internet -of- Things Networks

This work proposes a novel design for an active reconfigurable intelligent surface (RIS)-assisted digital twin (DT) based mobile edge computing (MEC) model that leverages edge intelligence to enhance ultra-reliable and low-latency communications (URLLC) services in Internet-of- Things (loT) networks. The system model considers uplink data transmission from the single antenna IoT-URLLC nodes (UNs) to a multi-antenna base station (BS) with the aid of an active RIS under imperfect channel state information (CSI). We formulate a total end-to-end (E2E) latency minimization problem for the proposed system model. An efficient alternating optimization (AO) algorithm is proposed to tackle the non-convexity of the problem by reformulating it into five subproblems: beamforming design, caching and offloading policies optimization, joint communication and computation optimization, and active RIS phase shift optimization. Simulation results demonstrate that the proposed DT-assisted optimal-phase active RIS scheme consistently outperforms benchmark schemes, such as optimal-phase passive RIS, random-phase active RIS, and no- RIS systems, considering factors such as imperfect CSI, power budget, number of RIS elements, the caching capacity of edge computing server (ECS) and the number of loT UNs.