Intelligent Reflecting Surface Aided AirComp: Multi-Timescale Design and Performance Analysis

TL;DR

This paper introduces a multi-timescale protocol for IRS-aided AirComp that reduces signaling overhead and analyzes its performance, showing favorable scaling laws and power control strategies for large systems.

Contribution

It proposes a novel multi-timescale transmission protocol for IRS-aided AirComp that significantly reduces signaling overhead and provides theoretical performance analysis.

Findings

Achieves MSE scaling of O(K/(N^2 M)) with system parameters.

Channel-inversion power control is asymptotically optimal for large N.

Full power transmission is unnecessary for energy-limited devices.

Abstract

The integration of intelligent reflecting surface (IRS) into over-the-air computation (AirComp) is an effective solution for reducing the computational mean squared error (MSE) via its high passive beamforming gain. Prior works on IRS aided AirComp generally rely on the full instantaneous channel state information (I-CSI), which is not applicable to large-scale systems due to its heavy signalling overhead. To address this issue, we propose a novel multi-timescale transmission protocol. In particular, the receive beamforming at the access point (AP) is pre-determined based on the static angle information and the IRS phase-shifts are optimized relying on the long-term statistical CSI. With the obtained AP receive beamforming and IRS phase-shifts, the effective low-dimensional I-CSI is exploited to determine devices' transmit power in each coherence block, thus substantially reducing the signalling overhead. Theoretical analysis unveils that the achievable MSE scales on the order of ${\cal O}\left( {K/\left( {{N^2}M} \right)} \right)$, where $M$, $N$, and $K$ are the number of AP antennas, IRS elements, and devices, respectively. We also prove that the channel-inversion power control is asymptotically optimal for large $N$, which reveals that the full power transmission policy is not needed for lowering the power consumption of energy-limited devices.