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Updated: Sep 9, 2025

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Interference Signal Suppression Algorithm Based on CNN-LSTM Model.

Ningbo Xiao1, Zuxun Song1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a deep learning algorithm using CNN-LSTM for interference signal suppression in wireless systems. The method effectively reduces interference, enhancing sensor reliability and communication quality.

Area of Science:

  • Signal Processing
  • Deep Learning
  • Wireless Communication

Background:

  • Sensor anti-interference capability is crucial for measurement accuracy, reliability, and stability.
  • Complex environments expose sensors to various interference sources, impacting performance.
  • Effective interference suppression is key to improving sensor operation and communication quality.

Purpose of the Study:

  • To propose a CNN-LSTM-based algorithm for suppressing interference signals in wireless communication systems.
  • To enhance the anti-interference capabilities of sensors through deep learning.
  • To validate the algorithm's effectiveness in diverse interference scenarios.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) for spatial feature extraction.
Keywords:
CNN-LSTMinterference signalsuppression algorithm

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Last Updated: Sep 9, 2025

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  • Employed Long Short-Term Memory (LSTM) networks for temporal dynamic characteristic capture.
  • Developed a CNN-LSTM model for interference signal prediction and suppression.

    • Main Results:

    • The CNN-LSTM algorithm demonstrated small error and high regression fitting compared to LSTM, BO-LSTM, and CNN-GRU.
    • Experimental simulations confirmed the algorithm's performance under various interference conditions.
    • Validation using ITU-R P.1546 and real-world noise datasets confirmed significant interference suppression.

    Conclusions:

    • The proposed CNN-LSTM algorithm effectively suppresses interference signals and environmental noise.
    • This deep learning approach enhances the robustness and reliability of wireless communication systems and sensors.
    • The findings provide a foundation for developing more advanced, interference-resilient sensor technologies.