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Machine-learning-based high-resolution DOA measurement and robust directional modulation for hybrid analog-digital massive MIMO transceiver

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Abstract

At hybrid analog-digital (HAD) transceiver, an improved HAD estimation of signal parameters via rotational invariance techniques (ESPRIT), called I-HAD-ESPRIT, is proposed to measure the direction of arrival (DOA) of a desired user, where the phase ambiguity due to HAD structure is dealt with successfully. Subsequently, a machine-learning (ML) framework is proposed to improve the precision of measuring DOA. Meanwhile, we find that the probability density function (PDF) of DOA measurement error (DOAME) can be approximated as a Gaussian distribution by the histogram method in ML. Then, a slightly large training data set (TDS) and a relatively small real-time set (RTS) of DOA are formed to predict the mean and variance of DOA/DOAME in the training stage and real-time stage, respectively. To improve the precisions of DOA/DOAME, three weight combiners are proposed to combine the-maximum-likelihood-learning outputs of TDS and RTS. Using the mean and variance of DOA/DOAME, their PDFs can be given directly, and we propose a robust beamformer for directional modulation (DM) transmitter with HAD by fully exploiting the PDF of DOA/DOAME, especially a robust analog beamformer on RF chain. Simulation results show that: (1) the proposed I-HAD-ESPRIT can achieve the HAD Cramer-Rao lower bound (CRLB); (2) the proposed ML framework performs much better than the corresponding real-time one without training stage; (3) the proposed robust DM transmitter can perform better than the corresponding non-robust ones in terms of secrecy rate.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Nos. 61771244, 61871229).

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Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhihong Zhuang, Ling Xu, Jiayu Li, Linlin Sun & Feng Shu

  2. The College of Physics and Information, Fuzhou University, Fuzhou, 350108, China

    Jinsong Hu

  3. School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NT, UK

    Jiangzhou Wang

Authors
  1. Zhihong Zhuang
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  2. Ling Xu
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  3. Jiayu Li
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  4. Jinsong Hu
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  5. Linlin Sun
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  6. Feng Shu
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  7. Jiangzhou Wang
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Correspondence to Feng Shu.

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Zhuang, Z., Xu, L., Li, J. et al. Machine-learning-based high-resolution DOA measurement and robust directional modulation for hybrid analog-digital massive MIMO transceiver. Sci. China Inf. Sci. 63, 180302 (2020). https://doi.org/10.1007/s11432-019-2921-x

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