Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD), we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.

翻译：大规模免授权传输与无小区无线通信系统已成为海量机器类通信的关键使能技术。本文提出一种基于深度展开的联合活跃度与数据检测（DU-JAD）算法，用于无小区系统中的大规模免授权传输。我们首先建立一个联合活跃度与数据检测优化问题，并采用前向后向分裂（FBS）进行近似求解。随后，对FBS应用深度展开技术，利用机器学习优化算法参数。为提升数据检测（DD）性能、降低算法复杂度并增强活跃用户检测（AUD）能力，我们分别采用动量策略、近似后验均值估计器以及新型软输出AUD模块。仿真结果验证了DU-JAD在AUD与DD任务中的有效性。