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

TL;DR

This work tackles energy-efficient task management for multi-access edge computing in the presence of obstructed links by introducing an active STAR-RIS-assisted system. By jointly optimizing partial task offloading, amplitude, phase shifts, amplification, BS power, and admitted tasks, and by decomposing the non-convex problem into three subproblems solved with sequential fractional programming, convex optimization, and Lyapunov optimization aided by a DDQN, the approach achieves superior energy efficiency and queue stability. The method is validated against multiple baselines, showing significant energy reductions (up to about 30% under various scenarios) and demonstrating the value of amplification and dual-region operation in active STAR-RIS. The results highlight the practical potential of active STAR-RIS in MEC to mitigate multiplicative fading and enhance edge performance for data-intensive applications.

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.

Figures (10)

• Figure 1: System model of active STAR-RIS-assisted MEC system.
• Figure 2: DDQN architecture.
• Figure 3: Performance comparison of cumulative rewards with different benchmark scenarios.
• Figure 4: Performance comparison of cumulative rewards with different solution methods.
• Figure 5: Performance comparison of total energy consumption under different input data size.