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Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels.

Xiuqi Deng1,2, Xin Bian1, Mingqi Li1

  • 1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

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|January 11, 2022
PubMed
Summary
This summary is machine-generated.

Faster-than-Nyquist (FTN) transmission enhances spectrum efficiency but causes inter-symbol interference (ISI). A novel joint data and model-driven detection algorithm improves FTN signal detection in multipath channels, outperforming existing methods.

Keywords:
FTN signalingdata-driven and model-driven combinationdeepearningdetectionmultipathneural network adaptability

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Area of Science:

  • Telecommunications Engineering
  • Signal Processing
  • Machine Learning in Communications

Background:

  • Faster-than-Nyquist (FTN) transmission is crucial for 6G, offering high spectrum efficiency.
  • FTN systems introduce significant inter-symbol interference (ISI), degrading traditional receiver performance, especially in harsh channel conditions.
  • Existing data-driven algorithms excel in Additive White Gaussian Noise (AWGN) channels but lack generalization for multipath environments.

Purpose of the Study:

  • To propose a joint data and model-driven detection (DMD-JD) algorithm for FTN signaling in multipath channels.
  • To address the performance limitations of current FTN detection methods in complex channel environments.
  • To enhance the generalization capability and practical applicability of deep learning-based FTN detection.

Main Methods:

  • A hybrid approach combining traditional linear equalizers (MMSE or ZF) with deep learning networks (CNN or LSTM).
  • The algorithm first performs channel equalization and then utilizes a deep learning network to mitigate severe ISI introduced by FTN.
  • Training and testing the deep learning network across different channel models to ensure robustness.

Main Results:

  • The proposed DMD-JD algorithm demonstrates superior performance in multipath channels compared to purely model-driven or data-driven approaches.
  • The deep learning network shows effective generalization, adapting well to FTN signal detection across various channel models after training on a single model.
  • The algorithm successfully mitigates the performance degradation caused by ISI in high compression rate FTN systems.

Conclusions:

  • The DMD-JD algorithm offers a robust and practical solution for FTN signal detection in challenging multipath environments.
  • Joint data and model-driven approaches significantly improve the performance and adaptability of deep learning in FTN signal processing.
  • This work enhances the engineering feasibility of advanced FTN technologies for future wireless communication systems like 6G.