Active STAR-RIS Assisted Digital Twin-based URLLC Internet-of-Things Networks

This paper presents a novel design for a mobile edge computing (MEC) service that integrates digital twin technology with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This configuration leverages edge intelligence, aiming to strengthen ultra-reliable and low-latency communications (URLLC) within Internet-of-Things (IoT) frameworks. We explore the uplink data transmission path from singular-antenna IoT URLLC nodes (UNs) to a multi-antenna base station (BS) facilitated by an active STAR-RIS. Our focus is on framing an end-to-end (e2e) latency reduction strategy for the presented system. Due to the inherent non-convexity of this problem, we propose an efficient alternating optimization (AO) algorithm to get a solution. This algorithm decomposes the main problem into five distinct sub-problems: transmit beamforming design, optimization of caching and offloading policies, joint communication and computation optimization, and enhancement of active STAR-RIS beamforming. An extensive set of simulation outcomes indicates that our DT-enhanced optimal-phase STAR-RIS approach consistently surpasses benchmark methods, particularly when accounting for variables such as power constraints, the number of RIS elements, the caching capacity of the edge computing server (ECS), and the number of IoT UNs.