Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces a dynamic, energy-efficient MEC algorithm leveraging RISs to optimize resource allocation and reduce latency in beyond 5G networks, adapting to changing conditions with low complexity.

Contribution

It presents a novel stochastic optimization-based algorithm that jointly manages radio, computation, and RIS parameters for dynamic MEC environments with unknown statistics.

Findings

Improved end-to-end delay performance.

Effective adaptation to time-varying channels.

Enhanced MEC performance with RIS optimization.

Abstract

In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end (E2E) delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS empowered MEC.