Joint Active User Detection, Channel Estimation, and Data Detection for Massive Grant-Free Transmission in Cell-Free Systems

TL;DR

This paper introduces a joint framework for active user detection, channel estimation, and data detection in cell-free massive grant-free systems, improving performance in machine-type communications.

Contribution

It proposes a novel optimization-based approach with a box-constrained forward-backward splitting algorithm for joint AUD, CE, and DD in cell-free systems.

Findings

Enhanced detection and estimation accuracy demonstrated in simulations

Significant performance improvements over existing methods

Effective handling of user activity and channel sparsity

Abstract

Cell-free communication has the potential to significantly improve grant-free transmission in massive machine-type communication, wherein multiple access points jointly serve a large number of user equipments to improve coverage and spectral efficiency. In this paper, we propose a novel framework for joint active user detection (AUD), channel estimation (CE), and data detection (DD) for massive grant-free transmission in cell-free systems. We formulate an optimization problem for joint AUD, CE, and DD by considering both the sparsity of the data matrix, which arises from intermittent user activity, and the sparsity of the effective channel matrix, which arises from intermittent user activity and large-scale fading. We approximately solve this optimization problem with a box-constrained forward-backward splitting algorithm, which significantly improves AUD, CE, and DD performance. We demonstrate the effectiveness of the proposed framework through simulation experiments.