AAT model based channel estimation for mmWave massive MIMO systems

被引：0

作者：

Yu S. ^{[1
]}

Liu R. ^{[1
]}

Zhang Y. ^{[1
]}

Xie N. ^{[1
]}

Huang L. ^{[1
]}

机构：

[1] College of Electronic and Optical Engineering, College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing

来源：

Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 03期

基金：

中国国家自然科学基金;

关键词：

attention in attention network; channel estimation; massive MIMO channel; temporal convolutional neural network;

D O I：

10.11959/j.issn.1000-436x.2024067

中图分类号：

学科分类号：

摘要：

To solve the problems of temporal correlation and susceptibility to noise in millimeter wave massive MIMO channels, which result in decreased channel estimation accuracy, a novel channel estimation method based on an improved temporal convolutional network was proposed. The channel matrices obtained from simulation were feed into the system as two-dimensional image data. The temporal correlation was utilized for feature fusion and an attention in attention network was constructed to enhance the system’s ability to extract deep channel features. Then, AAN was integrated into the temporal convolutional network for training. Finally, the system outputted a denoised two-dimensional image, namely, the channel estimation matrix. Simulation results demonstrate that the proposed method not only exhibits good performance and complexity compared to conventional channel estimation methods but also maintains robustness when the test scenario changes. © 2024 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：41 / 49

页数：8

共 18 条

[1]

ZHAO J H, NI S J, YANG L H, Et al., Multiband cooperation for 5G HetNets: a promising network paradigm, IEEE Vehicular Technology Magazine, 14, 4, pp. 85-93, (2019)

[2]

ABDALLAH A, CELIK A, MANSOUR M M, Et al., Deep learning-based frequency-selective channel estimation for hybrid mmWave MIMO systems, IEEE Transactions on Wireless Communications, 21, 6, pp. 3804-3821, (2022)

[3]

LI X F, ALKHATEEB A., Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems, Proceedings of the 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pp. 800-805, (2019)

[4]

DONG P H, ZHANG H, LI G Y., Framework on deep learning-based joint hybrid processing for mmWave massive MIMO systems, IEEE Access, 8, pp. 106023-106035, (2020)

[5]

QIAO Y T, YU S Y, SU P C, Et al., Research on an iterative algorithm of LS channel estimation in MIMO OFDM systems, Proceedings of the IEEE 6th Circuits and Systems Symposium on Emerging Technologies: Frontiers of Mobile and Wireless Communication, pp. 729-732, (2004)

[6]

RAO X B, LAU V K N., Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems, IEEE Transactions on Signal Processing, 62, 12, pp. 3261-3271, (2014)

[7]

FAN D, GAO F F, LIU Y W, Et al., Angle domain channel estimation in hybrid millimeter wave massive MIMO systems, IEEE Transactions on Wireless Communications, 17, 12, pp. 8165-8179, (2018)

[8]

DUONG N S, NGUYEN Q T, NGO K H, Et al., Sparse Bayesian learning with atom refinement for mmWave MIMO channel estimation, Proceedings of the 2023 IEEE Statistical Signal Processing Workshop (SSP), pp. 155-159, (2023)

[9]

DONG P H, ZHANG H, LI G Y, Et al., Deep CNN-based channel estimation for mmWave massive MIMO systems, IEEE Journal of Selected Topics in Signal Processing, 13, 5, pp. 989-1000, (2019)

[10]

WANG T Q, WEN C K, JIN S, Et al., Deep learning-based CSI feedback approach for time-varying massive MIMO channels, IEEE Wireless Communications Letters, 8, 2, pp. 416-419, (2019)

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