Structured-Sparsity-Aware Joint User Activity Detection and Channel Estimation for OTFS-Based Grant-Free Random Access

2026-08-04

AI Summary

This work addresses the reliability challenges in user activity detection and channel estimation for grant-free random access with Orthogonal Time Frequency Space (OTFS) modulation under high mobility, where doubly selective channels severely degrade performance. Leveraging the structured sparsity of delay–Doppler domain channels, the joint detection and estimation problem is formulated as a structured compressive sensing task. The paper innovatively identifies and integrates two sources of sparsity: the common sparsity across multiple antennas and the activation sparsity inherent in massive machine-type communication (mMTC) users, thereby constructing a hierarchical sparsity model. A structured sparse expectation propagation (SS-EP) algorithm is then developed based on a two-layer factor graph representation. Simulation results demonstrate that the proposed approach significantly outperforms existing benchmarks in both user detection accuracy and channel estimation precision.

Abstract

Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and channel estimation are critical challenges, particularly when orthogonal time-frequency space (OTFS) modulation is integrated with GFRA to address doubly selective channels induced by high mobility. In this paper, we propose an OTFS-based GFRA framework that exploits the inherent structured sparsity of delay-Doppler channels. By adopting a basis expansion model (BEM), we formulate joint user activity detection and channel estimation as a structured compressive sensing problem. A bi-level sparsity structure is identified, consisting of common sparsity across multiple receive antennas and activation sparsity across mMTC users. To effectively leverage this structure, we construct a two-layer factor graph and develop a structured sparsity expectation propagation (SS-EP) algorithm for efficient Bayesian inference. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmarks.

Problem

• grant-free random access
• user activity detection
• channel estimation
• OTFS modulation
• structured sparsity

Innovation

• structured sparsity
• OTFS modulation
• grant-free random access
• compressive sensing
• expectation propagation