Active STAR-RIS Empowered Edge System for Enhanced Energy Efficiency and Task Management

TL;DR

This paper introduces an active STAR-RIS-assisted edge computing system that optimizes energy efficiency and task management through advanced joint optimization techniques, outperforming traditional RIS systems in performance.

Contribution

It presents a novel active STAR-RIS-based MEC system with a comprehensive optimization framework for energy-efficient task offloading and resource allocation.

Findings

Outperforms benchmark schemes by 18.64% and 30.43% in key metrics.

Effectively minimizes energy consumption while maintaining task queue stability.

Demonstrates significant performance improvements over conventional RIS-assisted MEC systems.

Abstract

The proliferation of data-intensive and low-latency applications has driven the development of multi-access edge computing (MEC) as a viable solution to meet the increasing demands for high-performance computing and storage capabilities at the network edge. Despite the benefits of MEC, challenges such as obstructions cause non-line-of-sight (NLoS) communication to persist. Reconfigurable intelligent surfaces (RISs) and the more advanced simultaneously transmitting and reflecting (STAR)-RISs have emerged to address these challenges; however, practical limitations and multiplicative fading effects hinder their efficacy. We propose an active STAR-RIS-assisted MEC system to overcome these obstacles, leveraging the advantages of active STAR-RIS. The main contributions consist of formulating an optimization problem to minimize energy consumption with task queue stability by jointly optimizing the partial task offloading, amplitude, phase shift coefficients, amplification coefficients, transmit power of the base station (BS), and admitted tasks. Furthermore, we decompose the non-convex problem into manageable sub-problems, employing sequential fractional programming for transmit power control, convex optimization technique for task offloading, and Lyapunov optimization with double deep Q-network (DDQN) for joint amplitude, phase shift, amplification, and task admission. Extensive performance evaluations demonstrate the superiority of the proposed system over benchmark schemes, highlighting its potential for enhancing MEC system performance. Numerical results indicate that our proposed system outperforms the conventional STAR-RIS-assisted by 18.64\% and the conventional RIS-assisted system by 30.43\%, respectively.