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Deep Hybrid Learning Model for Fading Mitigation in MC-DCSK Receivers.

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Title: Deep Hybrid Learning Model for Fading Mitigation in MC-DCSK Receivers.
Authors: Hue, Ta Thi Kim1 (AUTHOR), Dat, Nguyen Thanh1 (AUTHOR), Tien, Nguyen Hoang1 (AUTHOR), Quyen, Nguyen Xuan1 (AUTHOR) quyen.nguyenxuan@hust.edu.vn
Source: International Journal of Bifurcation & Chaos in Applied Sciences & Engineering. May2026, Vol. 36 Issue 6, p1-20. 20p.
Subjects: Bit error rate, Signal reconstruction, Signal processing, Wireless communications, Electronic modulation, Deep learning
Abstract: In Multicarrier Differential Chaos Shift Keying (MC-DCSK) systems, a reference chaotic sequence is transmitted over a dedicated sub-carrier, while information-bearing chaotic sequences are transmitted over the remaining sub-carriers. At the receiver, the reference sequence is first recovered and then correlated with each of the data-carrying chaotic sequences to extract the transmitted information. However, due to the intrinsic sensitivity of chaotic signals to additive noise and channel fading, the accurate reconstruction of the reference sequence becomes highly challenging, leading to significant degradation in overall system performance. To address this issue, we propose a Hybrid UNet-Transformer architecture designed to enhance the robustness of MC-DCSK receivers under fading and noise conditions. The model combines 1D-convolutional blocks inspired by UNet for effective local feature extraction, with a patch-based Transformer encoder that captures global dependencies through positional encoding. This architecture processes long and complex chaotic sequences via overlapped chunking, supported by gradient clipping and adaptive learning rate scheduling to ensure stable and scalable training. The proposed model is assessed using diverse datasets that represent various channel conditions and modulation parameter settings. Experimental results, assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Signal-to-Noise Ratio (SNR), Structural Similarity Index (SSIM), correlation coefficient and Bit Error Rate (BER), demonstrate that the proposed model significantly improves reference sequence reconstruction and enhances the BER performance of the MC-DCSK system in realistic wireless environments. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company 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: 192586355
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Deep Hybrid Learning Model for Fading Mitigation in MC-DCSK Receivers.
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  Data: <searchLink fieldCode="AR" term="%22Hue%2C+Ta+Thi+Kim%22">Hue, Ta Thi Kim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dat%2C+Nguyen+Thanh%22">Dat, Nguyen Thanh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tien%2C+Nguyen+Hoang%22">Tien, Nguyen Hoang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Quyen%2C+Nguyen+Xuan%22">Quyen, Nguyen Xuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> quyen.nguyenxuan@hust.edu.vn</i>
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  Data: <searchLink fieldCode="DE" term="%22Bit+error+rate%22">Bit error rate</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+modulation%22">Electronic modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In Multicarrier Differential Chaos Shift Keying (MC-DCSK) systems, a reference chaotic sequence is transmitted over a dedicated sub-carrier, while information-bearing chaotic sequences are transmitted over the remaining sub-carriers. At the receiver, the reference sequence is first recovered and then correlated with each of the data-carrying chaotic sequences to extract the transmitted information. However, due to the intrinsic sensitivity of chaotic signals to additive noise and channel fading, the accurate reconstruction of the reference sequence becomes highly challenging, leading to significant degradation in overall system performance. To address this issue, we propose a Hybrid UNet-Transformer architecture designed to enhance the robustness of MC-DCSK receivers under fading and noise conditions. The model combines 1D-convolutional blocks inspired by UNet for effective local feature extraction, with a patch-based Transformer encoder that captures global dependencies through positional encoding. This architecture processes long and complex chaotic sequences via overlapped chunking, supported by gradient clipping and adaptive learning rate scheduling to ensure stable and scalable training. The proposed model is assessed using diverse datasets that represent various channel conditions and modulation parameter settings. Experimental results, assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Signal-to-Noise Ratio (SNR), Structural Similarity Index (SSIM), correlation coefficient and Bit Error Rate (BER), demonstrate that the proposed model significantly improves reference sequence reconstruction and enhances the BER performance of the MC-DCSK system in realistic wireless environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company 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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      – Type: doi
        Value: 10.1142/S0218127426500689
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      – Code: eng
        Text: English
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        PageCount: 20
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    Subjects:
      – SubjectFull: Bit error rate
        Type: general
      – SubjectFull: Signal reconstruction
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Wireless communications
        Type: general
      – SubjectFull: Electronic modulation
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Deep Hybrid Learning Model for Fading Mitigation in MC-DCSK Receivers.
        Type: main
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          Name:
            NameFull: Hue, Ta Thi Kim
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            NameFull: Dat, Nguyen Thanh
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            NameFull: Tien, Nguyen Hoang
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            NameFull: Quyen, Nguyen Xuan
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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            – TitleFull: International Journal of Bifurcation & Chaos in Applied Sciences & Engineering
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