Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches

Chenhao Qi; Peihao Dong; Wenyan Ma; Hua Zhang; Zaichen Zhang; Geoffrey Ye Li

Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches

Signal Processing 2022-03-15 v2 Machine Learning

Authors: Chenhao Qi , Peihao Dong , Wenyan Ma , Hua Zhang , Zaichen Zhang , Geoffrey Ye Li

Abstract

The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on CSI acquisition, including beam training and channel estimation for mmWave massive multiple-input multiple-output systems. The beam training can avoid the estimation of a high-dimension channel matrix while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead.

Keywords

channel state information mmwave massive mimo beam training channel estimation machine learning spectral efficiency computational complexity

Cite

@article{arxiv.2006.08894,
  title  = {Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches},
  author = {Chenhao Qi and Peihao Dong and Wenyan Ma and Hua Zhang and Zaichen Zhang and Geoffrey Ye Li},
  journal= {arXiv preprint arXiv:2006.08894},
  year   = {2022}
}

Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches

Abstract

Keywords

Cite

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