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AMP-Net Based Beamspace Channel Estimation in mmWave Massive MIMO Systems

Published: 16 May 2023 Publication History

Abstract

Due to the high channel dimensionality caused by the large number of antennas, the beamspace channel estimation is challenging in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) uplink systems. To enhance the channel estimation accuracy, a novel beamspace channel estimation method is presented by employing the benefit from the compressed sensing (CS). Specifically, the problem of beamspace channel estimation is firstly transformed into an underdetermined optimization problem based on the sparse characteristic of the beamspace channel. Then the approximate message passing (AMP) based on neural network is presented, which is called AMP-Net. When the handcrafted optimal transform is orthogonal in the AMP algorithm, a balanced convolutional neural network (CNN) is designed to select the beamspace channel estimation results under the guidance of the AMP algorithm. Finally, the iterative process is expanded into a deep network form. In the network, controllable parameters are introduced to increase the flexibility of the beamspace channel estimation process. Simulation results demonstrate that the proposed algorithm has better the estimation accuracy than conventional CS based algorithms and other network models.

References

[1]
M. Wang, F. Gao, S. Jin and H. Lin. 2019. An overview of enhanced massive MIMO with array signal processing techniques. IEEE Journal of Selected Topics in Signal Processing 13, 5 (2019), 886-901.
[2]
M. Cui and W. Zou. 2019. Low complexity joint hybrid precoding for millimeter wave MIMO systems. China Communications, 16, 2 (2019), 49-58.
[3]
P. Liu, Y. Li, W. Cheng, X. Gao and X. Huang. 2021. Intelligent reflecting surface aided NOMA for millimeter-wave massive MIMO with lens antenna array. IEEE Transactions on Vehicular Technology, 70, 5 (2021), 4419-4434.
[4]
F. Dong, W. Wang, Z. Huang and P. Huang. 2020. High-resolution angle-of-arrival and channel estimation for mmWave massive MIMO systems with lens antenna array. IEEE Transactions on Vehicular Technology, 69, 11 (2020), 12963–12973.
[5]
Mohades Z and Tabataba Vakili V. 2021. Deep neural network for compressive sensing and application to massive MIMO channel estimation. Circuits, Systems, and Signal Processing, 40, 9 (2021), 4474-4489.
[6]
C. -H. Chen, C. -R. Tsai, Y. -H. Liu, W. -L. Hung and A. -Y. Wu. 2016. Compressive sensing (CS) assisted low-complexity beamspace hybrid precoding for millimeter-wave MIMO systems. IEEE Transactions on Signal Processing, 65, 6 (2016), 1412- 1424.
[7]
A. Alkhateeb, O. El Ayach, G. Leus and R. W. Heath. 2014. Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE Journal of Selected Topics in Signal Processing, 8, 5 (2014), 831-846.
[8]
L. Wei and J. Zheng. 2019. Approximate message passing-aided iterative channel estimation and data detection of OFDM-IM in doubly selective channels. IEEE Access, 7 (2019), 133410-133420.
[9]
J. Kang, C. Chun and I. Kim. 2018. Deep-learning-based channel estimation for wireless energy transfer. IEEE Communicatios Letters, 22, 11 (2019), 2310- 2313.
[10]
Y. Zhang, Y. Mu, Y. Liu, T. Zhang and Y. Qian. 2020. Deep learning-based beamspace channel estimation in mmWave massive MIMO systems. IEEE Wireless Communications Letters, 9, 12 (2020): 2212-2215.
[11]
M. Borgerding, P. Schniter and S. Rangan. 2017. AMP-inspired deep networks for sparse linear inverse problems. IEEE Transactions on Signal Processing, 65, 16 (2017), 4293-4308.
[12]
Y. Wei, M. -M. Zhao, M. Zhao, M. Lei and Q. Yu. 2019. An AMP-based network with deep residual learning for mmWave beamspace channel estimation. IEEE Wireless Communications Letters, 8, 4 (2019), 1289-1292.
[13]
H. He, C. Wen, S. Jin and G. Y. Li. 2018. Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wireless Communications Letters, 7, 5 (2018), 852-855.
[14]
X. Wei, C. Hu and L. Dai. 2020. Deep learning for beamspace channel estimation in millimeter-wave massive MIMO systems. IEEE Transactions on Communications, 69, 1 (2020), 182-193.
[15]
X. Gao, L. Dai, S. Han, C. I and X. Wang. 2017. Reliable beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array. IEEE Transactions on Wireless Communications, 16, 9 (2017), 6010-6021.
[16]
S. F. Cotter and B. D. Rao. 2002. Sparse channel estimation via matching pursuit with application to equalization. IEEE Transactions on Communications, 50, 3 (2002), 374-377.
[17]
S. G. Mallat and Zhifeng Zhang. 1993. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41, 12 (1993), 3397-3415.
[18]
S. K. Sahoo and A. Makur. 2015. Signal recovery from random measurements via extended orthogonal matching pursuit. IEEE Transactions on Signal Processing, 63, 10 (2015), 2572-2581.
[19]
Y. Liu, Z. Zhan, J. -F. Cai, D. Guo, Z. Chen and X. Qu. 2016. Projected iterative soft-thresholding algorithm for tight frames in compressed sensing magnetic resonance imaging. IEEE transactions on medical imaging, 35, 9 (2016), 2130-2140.

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    AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
    September 2022
    1221 pages
    ISBN:9781450396899
    DOI:10.1145/3573942
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 16 May 2023

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    Author Tags

    1. approximate message passing (AMP)
    2. beamspace channel estimation
    3. convolutional neural network (CNN)
    4. massive MIMO
    5. millimeter wave (mmWave)

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