Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

TL;DR

This paper introduces a deep-unfolded framework for joint active user detection, channel estimation, and data detection in grant-free cell-free wireless systems, enhancing performance and reducing complexity.

Contribution

It proposes a novel deep-unfolded algorithm with hyper-parameter optimization for efficient joint detection in massive grant-free cell-free communications.

Findings

Improved active user detection accuracy.

Reduced computational complexity.

Enhanced system performance in simulations.

Abstract

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.