Covariance-Matching Distributed Activity Detection in Wideband Cell-Free MIMO

Activity detection plays an important role in grantfree random access, a promising approach for handling a large number of users in use cases like massive machine type communication (mMTC). Existing activity detection algorithms cover various scenarios but overlook wideband distributed antenna systems, a practical configuration for next-generation wireless networks. When following conventional activity detection methods, sparse Bayesian learning (SBL) could be an option in this case. However, SBL-based methods for wideband systems lack the consistency of maximum likelihood estimation (MLE), resulting in unsatisfactory detection performance. This paper proposes a novel distributed activity detection framework for wideband cell-free multiple-input and multiple-output (MIMO). Specifically, we provide a novel uplink channel model for activity detection in wideband cell-free MIMO, accounting for asynchronous reception. Additionally, we present possible SBLbased methods, identifying their limitations, which motivates the development of a new approach for activity detection. Next, we propose a covariance-matching distributed activity detection framework that matches the sample covariance matrix to the estimated covariance matrix. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.