Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel

LIU Zhiyong; JIN Zihao; YANG Hongjuan; LIU Biao; TANG Xinfeng; LI Bo

doi:10.11999/JEIT231196

Volume 46 Issue 5

May 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2004-2010

LIU Zhiyong, JIN Zihao, YANG Hongjuan, LIU Biao, TANG Xinfeng, LI Bo. Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196

Citation:

LIU Zhiyong, JIN Zihao, YANG Hongjuan, LIU Biao, TANG Xinfeng, LI Bo. Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196

Citation:

LIU Zhiyong, JIN Zihao, YANG Hongjuan, LIU Biao, TANG Xinfeng, LI Bo. Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2004-2010. doi: 10.11999/JEIT231196

PDF( 3173 KB)

Deep Learning-based Joint Multi-branch Merging and Equalization Algorithm for Underwater Acoustic Channel

doi: 10.11999/JEIT231196

LIU Zhiyong^{1, 3, 4},
JIN Zihao¹,
YANG Hongjuan^{1
,
,},
LIU Biao²,
TANG Xinfeng²,
LI Bo¹

1.
School of Information Science and Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China
2.
Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China
3.
Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems, Weihai 264209, China
4.
Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems, Ministry of Industry and Information Technology, Weihai 264209, China

Funds: The Strategic Rocket Innovation Foundation (ZH2022007), The National Natural Science Foundation of China (61871148), The Major Scientific and Technological Innovation Project of Shandong Province of China (2020CXGC010705, 2021ZLGX05, 2022ZLGX04)

Received Date: 2023-10-31
Rev Recd Date: 2024-03-27

Available Online: 2024-05-07

Publish Date: 2024-05-30

Abstract

Abstract

To better solve the fading and severe inter-symbol interference problems in underwater acoustic channels, a Joint Multi-branch Merging and Equalization algorithm based on Deep Learning (JMME-DL) is proposed in this paper. The algorithm jointly implements multi-branch merging and equalization with the help of the nonlinear fitting ability of the deep learning network. The merging and equalization are not independent of each other, in the implementation of the algorithm, the total error is first calculated based on the total output of the deep learning network, and then the network parameters of each part are jointly adjusted with the total error, and the dataset is constructed based on the statistical underwater acoustic channel model. Simulation results show that the proposed algorithm achieves faster convergence speed and better BER performance compared to the existing algorithms, making it better adapted to underwater acoustic channels.
- Underwater acoustic communication,
- Deep learning,
- Underwater acoustic channel,
- Joint multi-branch merging and equalization

FullText(HTML)

References(12)

References

[1]	邵宗战. 现代水声通信技术发展探讨[J]. 科技创新与应用, 2022, 12(20): 152–155. doi: 10.19981/j.CN23-1581/G3.2022.20.036. SHAO Zongzhan. Discussion on the development of modern hydroacoustic communication technology[J]. Technology Innovation and Application, 2022, 12(20): 152–155. doi: 10.19981/j.CN23-1581/G3.2022.20.036.
[2]	XIE Lin, ZHAO Haili, TIAN Chengjun, et al. Comparison of several new improved variable-step LMS algorithms[C]. 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE), Xi’an, China, 2022: 229–233. doi: 10.1109/CACRE54574.2022.9834206.
[3]	GUO Xiaochen, ZHANG Youwen, and ZHENG Wei. Variable forgetting factor RLS algorithm for mobile single carrier SIMO underwater acoustic communication[C]. Proceedings of SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), Harbin, China, 2023: 1261519. doi: 10.1117/12.2674104.
[4]	CHENG Xing, LIU Dejun, WANG Chen, et al. Deep learning-based channel estimation and equalization scheme for FBMC/OQAM systems[J]. IEEE Wireless Communications Letters, 2019, 8(3): 881–884. doi: 10.1109/LWC.2019.2898437.
[5]	CHEN S, GIBSON G J, COWAN C F N, et al. Adaptive equalization of finite non-linear channels using multilayer perceptrons[J]. Signal Processing, 1990, 20(2): 107–119. doi: 10.1016/0165-1684(90)90122-F.
[6]	LAVANIA S, KUMAM B, MATEY P S, et al. Adaptive channel equalization using recurrent neural network under SUI channel model[C]. 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2015: 1–6. doi: 10.1109/ICIIECS.2015.7193035.
[7]	ZHANG Youwen, LI Junxuan, ZAKHAROV Y, et al. Deep learning based underwater acoustic OFDM communications[J]. Applied Acoustics, 2019, 154: 53–58. doi: 10.1016/j.apacoust.2019.04.023.
[8]	JARUWATANADILOK S. Underwater wireless optical communication channel modeling and performance evaluation using vector radiative transfer theory[J]. IEEE Press, 2008, 26(9): 1620–1627. doi: 10.1109/JSAC.2008.081202.
[9]	STOJANOVIC M, CATIPOVIC J, and PROAKIS J G. Adaptive multichannel combining and equalization for underwater acoustic communications[J]. The Journal of the Acoustical Society of America, 1993, 94(3): 1621–1631. doi: 10.1121/1.408135.
[10]	CHOI J W, RIEDL T J, KIM K, et al. Adaptive linear turbo equalization over doubly selective channels[J]. IEEE Journal of Oceanic Engineering, 2011, 36(4): 473–489. doi: 10.1109/JOE.2011.2158013.
[11]	LIU Zhiyong, WANG Yinghua, SONG Lizhong, et al. Joint adaptive combining and variable tap-length multiuser detector for underwater acoustic cooperative communication[J]. KSII Transactions on Internet and Information Systems, 2018, 12(1): 325–339. doi: 10.3837/tiis.2018.01.016.
[12]	QARABAQI P and STOJANOVIC M. Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels[J]. IEEE Journal of Oceanic Engineering, 2013, 38(4): 701–717. doi: 10.1109/JOE.2013.2278787.