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SCIENCE CHINA Information Sciences, Volume 64 , Issue 8 : 181301(2021) https://doi.org/10.1007/s11432-021-3247-2

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

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  • ReceivedFeb 24, 2021
  • AcceptedApr 13, 2021
  • PublishedJul 7, 2021

Abstract


Acknowledgment

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


References

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  • Figure 1

    (Color online) Hybrid architecture for mmWave MIMO transceiver.

  • Figure 2

    (Color online) CSI acquisition for mmWave massive MIMO.

  • Figure 3

    (Color online) Illustration of mmWave massive MIMO communications system.

  • Figure 4

    (Color online) Illustrations of partial beam sweeping and hierarchical beam training.

  • Figure 5

    (Color online) Framework on deep CNN-based channel estimation.

  • Figure 6

    (Color online) Comparisons of spectral efficiency for different schemes in terms of SNR.

  • Table 1  

    Table 1Comparisons of different channel estimation or beam training schemes

    Scheme name Computational complexity makecellOverhead
    makecellBeam trainingPartial beam sweeping [34] $\mathcal{O}(U(\frac{N_{\textrm{T}}}{2N_{\textrm{RF}}}+2)+4U^3)$ $2(\frac{N_{\textrm{T}}}{2N_{\textrm{RF}}}+3)$
    Hierarchical beam training [39] $\mathcal{O}(U\log_2~N_{\textrm{T}}+4U^3)$ $2U\log_2~N_{\textrm{T}}+U$
    MAB-based scheme [55] $\mathcal{O}(N_{\textrm{T}}^3)$ Depend on convergence speed
    makecellChannel estimationAdaptive CS-based scheme [64] $\mathcal{O}(U\log_2~N_{\textrm{T}}+4U^3)$ $2U\log_2~N_{\textrm{T}}+U$
    ESPRIT-based scheme [80]$\mathcal{O}(8UT^3+2UL^2(10L+5T))$ $T$
    Deep CNN-based scheme [91] $\mathcal{O}(UN_{\textrm{T}}^2+UN_{\textrm{T}}\sum_{l=1}^{L_{c}}F_l^2N_{l-1}N_{l})$ $T$
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