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Open Access

Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network

College of Computer Science, Sichuan University, Chengdu 610000, China
Sichuan Research Institute, Shanghai Jiao Tong University, Chengdu 610000, China
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Abstract

The squelch problem of ultra-short wave communication under non-stationary noise and low Signal-to-Noise Ratio (SNR) in a complex electromagnetic environment is still challenging. To alleviate the problem, we proposed a squelch algorithm for ultra-short wave communication based on a deep neural network and the traditional energy decision method. The proposed algorithm first predicts the speech existence probability using a three-layer Gated Recurrent Unit (GRU) with the speech banding spectrum as the feature. Then it gets the final squelch result by combining the strength of the signal energy and the speech existence probability. Multiple simulations and experiments are done to verify the robustness and effectiveness of the proposed algorithm. We simulate the algorithm in three situations: the typical Amplitude Modulation (AM) and Frequency Modulation (FM) in the ultra-short wave communication under different SNR environments, the non-stationary burst-like noise environments, and the real received signal of the ultra-short wave radio. The experimental results show that the proposed algorithm performs better than the traditional squelch methods in all the simulations and experiments. In particular, the false alarm rate of the proposed squelch algorithm for non-stationary burst-like noise is significantly lower than that of traditional squelch methods.

Keywords

squelchGated Recurrent Unit (GRU)ultra-short wave communication

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Big Data Mining and Analytics
Volume 6 Issue 1,
March 2023
Pages 106-114
DOI: 10.26599/BDMA.2022.9020025
Cite this article:
Xiang Y, Lv Y, Lei W, et al. Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network. Big Data Mining and Analytics, 2023, 6(1): 106-114. https://doi.org/10.26599/BDMA.2022.9020025

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Received: 13 July 2022
Accepted: 28 July 2022
Published: 24 November 2022
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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