Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing

TL;DR

This paper explores the critical control operations in RIS-assisted mobile edge computing, analyzing their impact on latency and energy efficiency through theoretical and simulation-based evaluations.

Contribution

It introduces a framework to evaluate the trade-offs of control overhead in RIS-aided MEC, balancing resource allocation, channel estimation, and signaling.

Findings

Optimal control overhead balances latency and resource availability.

Higher control resources improve reliability but increase latency.

Framework clarifies control-resource trade-offs in RIS-MEC systems.

Abstract

This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS. While usually ignored, the control aspects related to channel estimation (CE), resource allocation (RA), and control signaling play a fundamental role in the user-perceived delay and energy consumption. In general, the higher the resources involved in the control operations, the higher their reliability; however, this introduces an overhead, which reduces the number of resources available for computation offloading, possibly increasing the overall latency experienced. Conversely, a lower control overhead translates to more resources available for computation offloading but impacts the CE accuracy and RA flexibility. This paper establishes a basic framework for integrating the impact of control operations in the performance evaluation of the RIS-aided MEC paradigm, clarifying their trade-offs through theoretical analysis and numerical simulations.