用专家混合模型提升智能反射表面下的活动检测精度
Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems
- 融合深度展开与专家混合机制，自动选择最优通道处理方案
- 在混合信道衰落下性能优于传统协方差法与黑盒神经网络
- 适合复杂无线环境下大规模设备活动检测任务
在海量机器类通信的活动检测中，智能反射表面（IRS）显著提升了无直接基站连接设备的覆盖能力。然而，传统检测方法通常针对单一信道模型设计，难以反映实际场景复杂性，尤其在引入IRS的系统中。本文提出一种新方法，将模型驱动的深度展开与混合专家（MoE）框架结合。通过自动选择三个专家设计之一并应用于展开的投影梯度法，该方法无需预先知晓设备与基站间的信道类型。仿真结果表明，所提的MoE增强型深度展开方法在混合信道衰落条件下，超越了传统的协方差法和黑盒神经网络设计，展现出更优的检测性能。
原文摘要 · Abstract (English)
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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