A Unified Activity Detection Framework for Massive Access: Beyond the Block-Fading Paradigm

TL;DR

This paper introduces a unified framework for activity detection in wireless communications that accounts for continuous channel variations, surpassing traditional block-fading assumptions, and enhances robustness and performance in dynamic environments.

Contribution

The paper proposes a low-dimensional channel approximation framework and algorithms for robust activity detection, extending to scenarios with limited coherence and pilot hopping.

Findings

Significant performance improvements demonstrated through numerical examples.

Effective estimation of principal subspace for low-dimensional approximation.

Robust activity detection in highly dynamic wireless environments.

Abstract

The wireless channel changes continuously with time and frequency and the block-fading assumption, which is popular in many theoretical analyses, never holds true in practical scenarios. This discrepancy is critical for user activity detection in grant-free random access, where joint processing across multiple coherence blocks is undesirable, especially when the environment becomes more dynamic. In this paper, we develop a framework for low-dimensional approximation of the channel to capture its variations over time and frequency, and use this framework to implement robust activity detection algorithms. Furthermore, we investigate how to efficiently estimate the principal subspace that defines the low-dimensional approximation. We also examine pilot hopping as a way of exploiting time and frequency diversity in scenarios with limited channel coherence, and extend our algorithms to this case. Through numerical examples, we demonstrate a substantial performance improvement achieved by our proposed framework.