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

• URL: http://arxiv.org/abs/2502.20183v1
• Date: Thu, 27 Feb 2025 15:19:28 GMT
• Title: Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems
• Authors: Zeyi Ren, Qingfeng Lin, Jingreng Lei, Yang Li, Yik-Chung Wu,
• Abstract summary: This paper introduces a novel approach that combines model-driven deep unfolding with a mixture of experts (MoE) framework.<n>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 base station.<n> Simulation results demonstrate that the proposed MoE-augmented deep unfolding method surpasses the traditional covariance-based method and black-box neural network design.
• Score: 16.28429015815172
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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