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

Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang*, Olav Tirkkonen, Christoph Studer*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Original languageEnglish
Article number9617144
Number of pages17
JournalIEEE Transactions on Wireless Communications
DOIs
Publication statusPublished - 16 Nov 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Channel estimation
  • MIMO communication
  • Training
  • Antennas
  • Complexity theory
  • Wireless communication
  • Sparse matrices

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