Original scientific paper

https://doi.org/10.31803/tg-20250326031336

CNN-Based Spectrum Sensing Method for Low Probability of Detection Communication Systems

Jae-Hyeon Lee ; Department of Artificial Intelligence Software, Hanbat National University, Sejong, 30139, Republic of Korea
So-Yeon Jeon ; Department of Artificial Intelligence Software, Hanbat National University, Sejong, 30139, Republic of Korea
Eui-Rim Jeong ; Department of Artificial Intelligence Software, Hanbat National University, Sejong, 30139, Republic of Korea

Abstract

In recent years, the development of Low Probability of Detection (LPD) communication systems has gained significant attention as a means to enhance communication security. Consequently, the need for effective signal interception technologies capable of detecting such signals has also increased. This paper proposes a novel spectrum sensing method based on Convolutional Neural Networks (CNNs) to determine the presence or absence of signals. The proposed method addresses the limitations of conventional energy detection techniques that rely on fixed thresholds, by learning diverse signal patterns to enable more accurate detection. Received signals are first sampled at a high rate and transformed into frequency-domain representations using the Fast Fourier Transform (FFT). These frequency spectra are then accumulated over time to form two-dimensional spectrograms, which are used as input to the CNN model. The proposed CNN classifier comprises four convolutional layers, along with batch normalization and pooling layers. Simulation results demonstrate that the proposed approach consistently outperforms traditional threshold-based energy detection methods, achieving approximately a 2 dB performance gain across all SNR conditions. Under –6 dB SNR, the method achieves an improvement of about 35% in detection accuracy.

Keywords

Binary Classification; Convolutional Neural Network; Low Probability of Detection (LPD); Spectrogram; Spectrum Sensing

Hrčak ID:

335261

URI

https://hrcak.srce.hr/335261

Publication date:

15.12.2025.

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