Index Detection for Underwater Acoustic Multi-carrier Communication Based on Deep Bidirectional Long Short-term Memory Network

ZHU Yunan; XIE Fangtong; ZHANG Mingliang; WANG Biao; GE Huilin

doi:10.11999/JEIT210949

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 1984-1990

ZHU Yunan, XIE Fangtong, ZHANG Mingliang, WANG Biao, GE Huilin. Index Detection for Underwater Acoustic Multi-carrier Communication Based on Deep Bidirectional Long Short-term Memory Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1984-1990. doi: 10.11999/JEIT210949

Citation:

ZHU Yunan, XIE Fangtong, ZHANG Mingliang, WANG Biao, GE Huilin. Index Detection for Underwater Acoustic Multi-carrier Communication Based on Deep Bidirectional Long Short-term Memory Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1984-1990. doi: 10.11999/JEIT210949

Citation:

ZHU Yunan, XIE Fangtong, ZHANG Mingliang, WANG Biao, GE Huilin. Index Detection for Underwater Acoustic Multi-carrier Communication Based on Deep Bidirectional Long Short-term Memory Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1984-1990. doi: 10.11999/JEIT210949

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Index Detection for Underwater Acoustic Multi-carrier Communication Based on Deep Bidirectional Long Short-term Memory Network

doi: 10.11999/JEIT210949

College of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Funds: The National Natural Science Foundation of China (52071164), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_3161)

Received Date: 2021-09-07
Accepted Date: 2021-11-18
Rev Recd Date: 2021-11-11

Available Online: 2021-11-25

Publish Date: 2022-06-21

Abstract

Abstract

When the Index Modulated Filter Bank MultiCarrier (FBMC-IM) underwater acoustic communication system carries out signal detection, the first step is to determine the index of the active subcarriers according to the recovered data after equalization. In this paper, the advantage of Bidirectional Long Short-Term Memory (BLSTM) network for feature extraction of chronological signals is combined, the deep learning theory is introduced into the concept of underwater acoustic signal processing, and an index detection method based on deep BLSTM is proposed. The improved algorithm can increase the estimation accuracy by transforming the index detection into a data-driven multivariate classification. Compared with the traditional methods, the proposed algorithm has lower computational complexity but better bit error ratio performance. The superiority and robustness of the proposed method are verified by the simulation based on lake trial channel data, which can be considered as a general detection method under index modulation mechanism.
- Underwater acoustic communication,
- Deep learning,
- Filter Bank MultiCarrier (FBMC),
- Index Modulation (IM),
- Index detection

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References(13)

References

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