Robust Activity Detection for Massive Random Access

TL;DR

This paper introduces robust statistical methods for device activity detection in massive IoT networks, effectively handling non-Gaussian noise and outliers to improve detection accuracy and robustness.

Contribution

It develops novel robust algorithms that do not rely on Gaussian noise assumptions, with proven convergence and superior performance in impulsive noise environments.

Findings

Proposed algorithms outperform existing methods in non-Gaussian noise scenarios.

The coordinate-wise objective function is proven to be geodesically convex.

Numerical experiments show higher detection accuracy and robustness.

Abstract

Massive machine-type communications (mMTC) are fundamental to the Internet of Things (IoT) framework in future wireless networks, involving the connection of a vast number of devices with sporadic transmission patterns. Traditional device activity detection (AD) methods are typically developed for Gaussian noise, but their performance may deteriorate when these conditions are not met, particularly in the presence of heavy-tailed impulsive noise. In this paper, we propose robust statistical techniques for AD that do not rely on the Gaussian assumption and replace the Gaussian loss function with robust loss functions that can effectively mitigate the impact of heavy-tailed noise and outliers. First, we prove that the coordinate-wise (conditional) objective function is geodesically convex and derive a fixed-point (FP) algorithm for minimizing it, along with convergence guarantees. Building on the FP algorithm, we propose two robust algorithms for solving the full (unconditional) objective function: a coordinate-wise optimization algorithm (RCWO) and a greedy covariance learning-based matching pursuit algorithm (RCL-MP). Numerical experiments demonstrate that the proposed methods significantly outperform existing algorithms in scenarios with non-Gaussian noise, achieving higher detection accuracy and robustness.