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Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

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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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 62071116, 61960206005).

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Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Chenhao Qi, Wenyan Ma, Hua Zhang & Zaichen Zhang

  2. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Peihao Dong

  3. Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK

    Geoffrey Ye Li

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  1. Chenhao Qi
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  2. Peihao Dong
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  3. Wenyan Ma
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  4. Hua Zhang
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  5. Zaichen Zhang
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  6. Geoffrey Ye Li
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Correspondence to Chenhao Qi or Geoffrey Ye Li.

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Qi, C., Dong, P., Ma, W. et al. Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches. Sci. China Inf. Sci. 64, 181301 (2021). https://doi.org/10.1007/s11432-021-3247-2

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