Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems
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Date
2025
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
73
Pages / Article No.
1094 - 1109
Publisher
IEEE
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Subject
Active user detection; cell-free communication; channel estimation; data detection; deep unfolding; grant-free transmission; massive machine-type communication