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A CNN‐BiLSTM–Based Deep Learning Model for CPM Signal Detection.

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Title: A CNN‐BiLSTM–Based Deep Learning Model for CPM Signal Detection.
Authors: He, Yang1 (AUTHOR), Cao, Ning2 (AUTHOR), Hu, Can2 (AUTHOR), Lu, Hao1 (AUTHOR) luhao@hhu.edu.cn
Source: Electronics Letters (Wiley-Blackwell). Jan2025, Vol. 61 Issue 1, p1-6. 6p.
Subjects: Convolutional neural networks, Continuous phase modulation, Detection algorithms, Deep learning, Signal processing, Long short-term memory, Mathematical optimization
Abstract: This letter proposes a convolutional neural network–bidirectional long short‐term memory (CNN‐BiLSTM) architecture for continuous phase modulation (CPM) signal detection by optimising the extraction of time‐frequency features and temporal dependencies with reduced complexity. It significantly outperforms the existing maximum likelihood sequence detection (MLSD) and CNN with fully connected layer (CNN‐FC) detectors in higher‐order modulation and multipath scenarios, achieving a 97.99% parameter reduction compared to CNN‐FC. Numerical results confirm its exceptional balance of detection performance and computational efficiency, making it ideal for complex channels and resource‐constrained systems. [ABSTRACT FROM AUTHOR]
Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 190527257
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  Data: A CNN‐BiLSTM–Based Deep Learning Model for CPM Signal Detection.
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  Data: <searchLink fieldCode="JN" term="%22Electronics+Letters+%28Wiley-Blackwell%29%22">Electronics Letters (Wiley-Blackwell)</searchLink>. Jan2025, Vol. 61 Issue 1, p1-6. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+phase+modulation%22">Continuous phase modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Group: Ab
  Data: This letter proposes a convolutional neural network–bidirectional long short‐term memory (CNN‐BiLSTM) architecture for continuous phase modulation (CPM) signal detection by optimising the extraction of time‐frequency features and temporal dependencies with reduced complexity. It significantly outperforms the existing maximum likelihood sequence detection (MLSD) and CNN with fully connected layer (CNN‐FC) detectors in higher‐order modulation and multipath scenarios, achieving a 97.99% parameter reduction compared to CNN‐FC. Numerical results confirm its exceptional balance of detection performance and computational efficiency, making it ideal for complex channels and resource‐constrained systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1049/ell2.70502
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      – Code: eng
        Text: English
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        PageCount: 6
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    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Continuous phase modulation
        Type: general
      – SubjectFull: Detection algorithms
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
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      – TitleFull: A CNN‐BiLSTM–Based Deep Learning Model for CPM Signal Detection.
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            NameFull: He, Yang
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            NameFull: Cao, Ning
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            NameFull: Hu, Can
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            NameFull: Lu, Hao
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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            – TitleFull: Electronics Letters (Wiley-Blackwell)
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