LSTM-Based Channel Estimation Method in Time-Varying Channels

被引：0
|
作者：
Ji C. [1 ,2 ]
Wang X. [1 ]
Geng R. [1 ]
Liang M.-J. [3 ]
机构：
[1] School of Computer Science & Engineering, Northeastern University, Shenyang
[2] Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang
[3] School of Information Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 11期
关键词：
channel estimation; deep learning; long short-term memory (LSTM); multilayer perceptron (MLP); time-varying channel;
D O I：
10.12068/j.issn.1005-3026.2023.11.001
中图分类号：
学科分类号：
摘要：
Aiming to address the limitations of traditional channel estimation methods in time-varying channel environments, as well as the low estimation accuracy or high complexity of deep learning-based channel estimation methods, a channel estimation network based on long short-term memory structure is proposed, which consists of a bidirectional long short-term memory (BiLSTM)network and a multilayer perceptron(MLP) network, namely BiLSTM-MLP. First, the BiLSTM network is used to learn the time-varying characteristics of the channel. Then, a MLP network is used to denoise and reconstruct the channel estimation. Simulation results show that the proposed channel estimation method has better performance than traditional methods, and has lower complexity and better performance compared with the same type of deep learning-based estimation methods. Furthermore, the proposed method is also robust to different pilot densities. © 2023 Northeastern University. All rights reserved.
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页码：1521 / 1528
页数：7
相关论文
共 15 条
  • [1] Usha S M, Mahesh H B., Least square channel estimation for image transmission with OFDM over fading channel [ J ], International Journal of Computer Networks and Communications, 12, 1, pp. 1-17, (2020)
  • [2] Nadeem Q U A, Kammoun A, Debbah M, Et al., Asymptotic analysis of RZF over double scattering channels with MMSE estimation [ J ], IEEE Transactions on Wireless Communications, 18, 5, pp. 2509-2526, (2019)
  • [3] Xue Qian, Applications of deep learning in the physical layer of wireless communication, (2020)
  • [4] Zhang S, Liu Y S, Gao F F, Et al., Deep learning based channel extrapolation for large-scale antenna systems: opportunities, challenges and solutions [ J ], IEEE Wireless Communications, 28, 6, pp. 160-167, (2021)
  • [5] Wu B, Li K H, Yang M L, Et al., A reverberation-time-aware approach to speech dereverberation based on deep neural networks[ J], IEEE / ACM Transactions on Audio,Speech and Language Processing, 25, 1, pp. 102-111, (2017)
  • [6] Farsad N, Andrea G., Neural network detection of data sequences in communication systems[ J], IEEE Transactions on Signal Processing, 66, 21, pp. 5663-5678, (2018)
  • [7] Han S, Oh Y, Song C., A deep learning based channel estimation scheme for IEEE 802. 11p systems [ C ], ICC 2019 - 2019 IEEE International Conference on Communications(ICC), pp. 1-6, (2019)
  • [8] Ye H, Li G Y, Juang B H., Power of deep learning for channel estimation and signal detection in OFDM systems[ J], IEEE Wireless Communications Letters, 7, 1, pp. 114-117, (2018)
  • [9] Wang S Y, Yao R, Tsiftsis T A, Et al., Signal detection in uplink time-varying OFDM systems using RNN with bidirectional LSTM [ J ], IEEE Wireless Communications Letters, 9, 11, pp. 1947-1951, (2020)
  • [10] Ma X L, Ye H, Li Y, Et al., Learning assisted estimation for time-varying channels [ C ], 2018 15th International Symposium on Wireless Communication Systems, pp. 1-5, (2018)
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