Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems

TL;DR

This paper explores how deploying intelligent reflecting surfaces (IRSs) can enhance over-the-air computation (AirComp) in large-scale C-RAN systems by optimizing IRS phases and detection to reduce estimation error.

Contribution

It introduces a joint optimization framework for IRS phases and PS detection in IRS-aided AirComp systems within C-RAN, demonstrating improved performance.

Findings

IRS deployment reduces mean squared error in AirComp

Optimized IRS phases significantly improve channel coherence

Numerical results confirm performance gains

Abstract

Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. AirComp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.