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.

翻译：无蜂窝通信有望显著提升大规模机器类型通信中的免授权传输性能，其中多个接入点协同服务大量用户设备，以改善覆盖范围与频谱效率。本文针对无蜂窝系统中大规模免授权传输问题，提出一种联合活跃用户检测（AUD）、信道估计（CE）与数据检测（DD）的新框架。通过同时考虑由间歇性用户活动导致的数据矩阵稀疏性，以及由间歇性用户活动与大尺度衰落共同导致的等效信道矩阵稀疏性，我们构建了联合AUD、CE与DD的优化问题。采用约束盒式前向后向分裂算法对该优化问题进行近似求解，显著提升了AUD、CE与DD的性能。仿真实验验证了所提框架的有效性。