Research on Low Complexity Underwater Acoustic Multiple Input Multiple Output Orthogonal Time Frequency Space Modulation Communication Method

被引：0
|
作者：
Wang B. [1 ]
Fang Z. [1 ]
Zhu Y. [1 ]
Guo X. [1 ]
Zhu B. [1 ]
机构：
[1] Ocean College, Jiangsu University of Science and Technology, Zhenjiang
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Multiple Input Multiple Output (MIMO); Orthogonal Time-Frequency-Space (OTFS) modulation; Underwater acoustic communication; Virtual Time Reversal Mirror (VTRM);
D O I：
10.11999/JEIT230183
中图分类号：
学科分类号：
摘要：
In the Multiple Input Multiple Output Orthogonal Time Frequency Space (MIMO-OTFS) underwater acoustic communication system, MIMO-OTFS communication based on the Message Passing (MP) algorithm have problems with high computational complexity, which may increase equipment costs in practical applications. To solve this problem, an MIMO-OTFS equalization algorithm based on two-dimensional Virtual Time Reversal Mirror (VTRM) is proposed, which uses the time-frequency-space focusing characteristics of VTRM to effectively improve the equalization performance. The channel estimation is performed using the Improved two-dimensional Proportional Normalized Least Mean Square (IPNLMS) algorithm, which utilizes the sparse characteristics of the time-delay Doppler domain channel to improve convergence speed at a lower computational complexity. Finally, residual inter-symbol interference is eliminated and system performance is further improved through the use of the two-dimensional adaptive decision feedback equalization algorithm. The simulation results demonstrate the feasibility of the proposed equalization algorithm, and show that it has lower complexity than the MP algorithm while ensuring the same performance. © 2024 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
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页码：83 / 91
页数：8
相关论文
共 24 条
  • [1] STOJANOVIC M, PREISIG J., Underwater acoustic communication channels: Propagation models and statistical characterization[J], IEEE Communications Magazine, 47, 1, pp. 84-89, (2009)
  • [2] AVRASHI G, AMAR A, COHEN I., Time-varying carrier frequency offset estimation in OFDM underwater acoustic communication, Signal Processing, 190, (2022)
  • [3] JIA Shuyang, ZOU Sichen, ZHANG Xiaochuan, Et al., Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform, Applied Acoustics, 192, (2022)
  • [4] ZHANG Yonglin, LI Chao, WANG Haibin, Et al., Deep learning aided OFDM receiver for underwater acoustic communications, Applied Acoustics, 187, (2022)
  • [5] WANG Zhizhan, LI Yuzhou, WANG Chengcai, Et al., A-OMP: An adaptive OMP algorithm for underwater acoustic OFDM channel estimation[J], IEEE Wireless Communications Letters, 10, 8, pp. 1761-1765, (2021)
  • [6] ZHU Yunan, XIE Fangtong, ZHANG Mingliang, Et al., Index detection for underwater acoustic multi-carrier communication based on deep bidirectional long short-term memory network[J], Journal of Electronics & Information Technology, 44, 6, pp. 1984-1990, (2022)
  • [7] WANG Tiejun, PROAKIS J G, MASRY E, Et al., Performance degradation of OFDM systems due to Doppler spreading[J], IEEE Transactions on Wireless Communications, 5, 6, pp. 1422-1432, (2006)
  • [8] HADANI R, RAKIB S, TSATSANIS M, Et al., Orthogonal time frequency space modulation[C], 2017 IEEE Wireless Communications and Networking Conference, pp. 1-6, (2017)
  • [9] REZAZADEHREYHANI A, FARHANG A, JI Mingyue, Et al., Analysis of discrete-time MIMO OFDM-based orthogonal time frequency space modulation[C], 2018 IEEE International Conference on Communications, pp. 1-6, (2018)
  • [10] LI Shuangyang, YUAN Jinhong, YUAN Weijie, Et al., Performance analysis of coded OTFS systems over high-mobility channels[J], IEEE Transactions on Wireless Communications, 20, 9, pp. 6033-6048, (2021)
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