Paper on Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO Systems published in the IEEE Transactions on Wireless Communications!

The paper “Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems” proposes a novel joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems. This JED algorithm reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). To this end, we propose to iteratively solve a relaxed version of a maximum a-posteriori JED problem that simultaneously exploits the sparsity of cell-free massive MU-MIMO channels and the boundedness of QAM constellations. In order to improve the performance and convergence of our algorithm, the paper proposes methods to permute the access point and UE indices to form so-called virtual cells. Simulation results 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.

The paper has been co-authored by Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, and Christoph Studer and will appear in the IEEE Transactions on Wireless Communications. You can find a preprint of the paper on external page arXiv.