Active Detection and Channel Estimation Schemes for Massive Random Access in User-Centric Cell-Free Massive MIMO System

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.