SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios^☆

Abstract

To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.

Keywords

mmWave massive MIMO / Internet of vehicles / Channel estimation / Squeeze-and-excitation attention / Residual learning

CRediT authorship contribution statement

Junhui Zhao: Writing - review & editing, Supervision, Project administration, Funding acquisition. Ruixing Ren: Writing - review & editing, Writing - original draft. Yao Wu: Writing - original draft. Qingmiao Zhang: Supervision. Wei Xu: Supervision. Dongming Wang: Supervision. Lisheng Fan: Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U2001213 and 62261024, in part by National Key Research and Development Project under Grant 2020YFB1807204, and in part by Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education under Grant KFKT-2022101.

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