BiLSTM-Based Frame Synchronization for Overlapped S-AIS Signals: A Learning-Empowered Approach

Tiancheng Yang*, Dongxuan He, Zhiping Lu, Hua Wang, Hongye Zhao, Zheng Wu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

In this paper, focusing on the signal detection of space-borne automatic identification system (S-AIS), two learning-empowered frame synchronization methods are proposed, which predict the accurate overlapping position of two S-AIS signals with the help of a bidirectional long short-term memory (BiLSTM) network. In particular, by regarding the frame synchronization as a binary classification issue, BiLSTM network can be utilized to find the overlapping position of the received signals accurately. Furthermore, convolutional neural network (CNN) is introduced into the proposed BiLSTM-based approach to handle the non-smooth power fluctuation. Simulation results show that our proposed learning-empowered methods outperform the conventional frame synchronization method in terms of accuracy and robustness, which can work effectively even under various communication conditions.

源语言英语
主期刊名2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9798350345384
DOI
出版状态已出版 - 2023
活动2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 - Dalian, 中国
期限: 10 8月 202312 8月 2023

出版系列

姓名2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

会议

会议2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
国家/地区中国
Dalian
时期10/08/2312/08/23

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