Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems

TL;DR

This paper proposes a novel MoE-augmented deep unfolding approach for activity detection in IRS-aided systems, effectively handling mixed channel conditions without prior channel knowledge, and outperforming traditional methods.

Contribution

It introduces a mixture of experts framework integrated with deep unfolding to adaptively select channel models, enhancing activity detection in complex IRS-assisted environments.

Findings

Outperforms traditional covariance-based methods

Achieves higher detection accuracy in mixed channel scenarios

Eliminates need for prior channel knowledge

Abstract

In the realm of activity detection for massive machine-type communications, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However, traditional activity detection methods are typically designed for a single type of channel model, which does not reflect the complexities of real-world scenarios, particularly in systems incorporating IRS. To address this challenge, this paper introduces a novel approach that combines model-driven deep unfolding with a mixture of experts (MoE) framework. By automatically selecting one of three expert designs and applying it to the unfolded projected gradient method, our approach eliminates the need for prior knowledge of channel types between devices and the BS. Simulation results demonstrate that the proposed MoE-augmented deep unfolding method surpasses the traditional covariance-based method and black-box neural network design, delivering superior detection performance under mixed channel fading conditions.