Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks

TL;DR

This paper introduces a proximal gradient unfolding neural network for joint activity detection and channel estimation in massive IoT networks, improving convergence, robustness, and adaptivity over existing methods.

Contribution

It formulates JADCE as a group-row-sparse matrix recovery problem and develops an accelerated, adaptive unfolding neural network with theoretical convergence guarantees.

Findings

Achieves better recovery performance than baseline algorithms.

Demonstrates faster convergence and improved robustness.

Provides theoretical proof of accelerated convergence.

Abstract

Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a critical issue. Although existing compressive sensing algorithms can be applied for JADCE, they usually fail to simultaneously harvest the following properties: effective sparsity inducing, fast convergence, robust to different pilot sequences, and adaptive to time-varying networks. To this end, we propose an unfolding framework for JADCE based on the proximal gradient method. Specifically, we formulate the JADCE problem as a group-row-sparse matrix recovery problem and leverage a minimax concave penalty rather than the widely-used $\ell_1$-norm to induce sparsity. We then develop a proximal gradient-based unfolding neural network that parameterizes the algorithmic iterations. To improve convergence rate, we incorporate momentum into the unfolding neural network, and prove the accelerated convergence theoretically. Based on the convergence analysis, we further develop an adaptive-tuning algorithm, which adjusts its parameters to different signal-to-noise ratio settings. Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.