This paper investigates the problem of joint active user detection (AUD) and channel estimation (CE) for grant-free random access in a cell-free massive multi-input multi-output (MIMO) system. Due to the sporadic activation of users and the channel block sparsity caused by large-scale fading in the cell-free system, the effective channel matrix exhibits a dual sparsity property. Considering this dual sparsity, joint AUD and CE are formulated as a sparse signal reconstruction problem based on compressed sensing. A ‘three-choice-one’ prior assumption is employed to extract the block sparsity in the antenna dimension. Then a variational Bayesian inference-based algorithm is proposed, and the simulation results validate the reliability of the proposed algorithm.
Variational Bayesian Inference-Based Joint Active User Detection and Channel Estimation in Cell-Free Massive MIMO
2024-06-24
1033749 byte
Conference paper
Electronic Resource
English