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

arXiv: https://arxiv.org/abs/2502.20183 · HTML: https://mlsanity.com/paper/2502.20183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts.
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Placed in

- Mixture-of-experts / [Expert routing strategies](https://mlsanity.com/moe/expert-routing-strategies/)

## Builds on (most cited first, 10 of 10)

- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Towards Understanding Mixture of Experts in Deep Learning](https://mlsanity.com/paper/2208.02813/) (2022)
- [Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection](https://mlsanity.com/paper/2112.10086/) (2021)
- [Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks](https://mlsanity.com/paper/2402.06942/) (2024)
- [Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms](https://mlsanity.com/paper/2210.00451/) (2022)
- [Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach](https://mlsanity.com/paper/2401.16775/) (2024)
- [Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems](https://mlsanity.com/paper/2312.12874/) (2023)
- [Reconfigurable Intelligent Surface for Massive Connectivity](https://mlsanity.com/paper/2101.10322/) (2021)
- [Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks](https://mlsanity.com/paper/2212.01839/) (2022)

## Abstract (arXiv)

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.