The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

中文翻译：

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以用户为中心的无小区大规模 MIMO 系统中大规模随机接入的主动检测和信道估计方案


下一代无线通信系统提出了更高的传输效率和更密集的用户接入的需求。为了适应未来通信的发展，本文重点研究以用户为中心的无小区大规模多输入多输出（MIMO）架构下的大规模随机接入方案。对于上行链路传输，设计了数据帧结构以实现活动用户检测（AUD）、信道估计（CE）和数据传输。接入点 (AP) 和用户设备 (UE) 之间的关联旨在促进以用户为中心的无小区可扩展架构。在本文中，提出了一种基于最大似然（ML）的AUD方法来获取活动UE的集合。通过设置合适的阈值并结合UE-AP关联，可以获得准确的主动检测结果。通过利用AUD模块中检测到的活动UE集，可以以较低的计算复杂度来完成CE。具体而言，CE算法采用基于广义近似消息传递的稀疏贝叶斯学习狄利克雷过程（GAMP-DP-SBL），利用AP的空间聚集和分散特性来提高估计精度。在GAMP-DP-SBL算法的基础上，提出了一种聚类算法（GAMP-CDP-SBL）来减少感知矩阵的规模并提高相关活动UE的CE精度。此外，为了增强系统的可扩展性，本文提出了去中心化的AUD和CE算法。各种参数设置和不同场景下的仿真结果显示了所提方案的优越性能。