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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350345384
DOIs
Publication statusPublished - 2023
Event2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 - Dalian, China
Duration: 10 Aug 202312 Aug 2023

Publication series

Name2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

Conference

Conference2023 IEEE/CIC International Conference on Communications in China, ICCC 2023
Country/TerritoryChina
CityDalian
Period10/08/2312/08/23

Keywords

  • BiLSTM
  • CNN
  • S-AIS communication systems
  • frame synchronization

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