Massive Random Access in Cell-Free Massive MIMO System
A user-centric massive random access scheme under the context of cell-free massive multiple-input multiple-output(MIMO)ar-chitecture is investigated.To achieve a scalable architecture,the association between access points(APs)and user equipment(UE)as well as the clustering method for APs is discussed.Regarding active UE detection(AUD),a class of maximum likelihood(ML)-based schemes is proposed to obtain active UE set.By adjusting the threshold,detection results with varying accuracies can be achieved.Leveraging the de-tected user set from AUD,the system employs sparse Bayesian learning based on Dirichlet process(DP-SBL)for channel estimation(CE),effectively utilizing the spatial clustering characteristics of APs to enhance accuracy.Building on this,a joint AUD and CE algorithm is pro-posed.Simulation results validate the superiority of the proposed approach in terms of performance.
massive random accesscell-free massive MIMOactive UE detectionchannel estimation
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