Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

• Main Authors: Goldstein, T. (Author), Song, H. (Author), Studer, C. (Author), Tirkkonen, O. (Author), You, X. (Author), Zhang, C. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: Bit error rate; Cell-free; Cell-free communication system; Cells; Channel estimation; Codes (symbols); Communications systems; Cost reduction; Cytology; Feedback control; Free communications; Iterative methods; joint channel estimation and data detection (JED); Joint channel estimation and data detection (JED); Joint channel estimation and data detections; massive multi-user (MU) multiple-input multiple-output (MIMO); Massive multi-user multiple-input multiple-output; MIMO systems; Multiple inputs; Multiple outputs; Multiusers; Telecommunication repeaters
• Online Access: View Fulltext in Publisher

 We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs. © 2002-2012 IEEE.