Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems
Seyed Hadi Mirfarshbafan, Christoph Studer
Abstract
We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on the number of users, basestation antennas, and channel sparsity for which beamspace equalization can be less complex than conventional antenna-domain processing. We evaluate the performance-complexity trade-offs of existing and new beamspace equalization algorithms using simulations with realistic mmWave channel models. Our results reveal that one of our proposed beamspace equalization algorithms achieves up to 8× complexity reduction under line-of-sight conditions, assuming a sufficiently large number of transmissions within the channel coherence interval.
BibTeX
@inproceedings{icassp2020_sparsebeamspacee,
title = {Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems},
author = {Seyed Hadi Mirfarshbafan and Christoph Studer},
booktitle = {ICASSP 2020},
year = {2020}
}