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The TC2NND spectrum sensing algorithm, which leverages cyclostationary features and Convolutional Neural Networks, achieves a 91.5% detection probability with a 5% false-alarm probability at a low signal-to-noise ratio (-10 dB), significantly outperforming conventional methods in maritime environments. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

July 28, 2025Entropy1 citationsOpen Access

Maritime Spectrum Sensing Based on Cyclostationary Features and Convolutional Neural Networks

XGXuan GengInstitute of Rock and Soil Mechanics BHBoyu HuUniversity of International Business and Economics

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Abstract

For maritime cognitive radio networks (MCRN), spectrum sensing (SS) is challenging due to the movement of the sea, channel interference, and unstable link quality. Besides the basic sensing capabilities that are required, SS in MCRN also requires the ability to adapt to complex and dynamic environments. By transforming spectrum sensing into a classification problem and leveraging cyclostationary features and Convolutional Neural Networks (CNN), This paper proposes a classification-guided TC2NND (Transfer Cyclostationary- feature and Convolutional Neural Networks Detection) spectrum sensing algorithm, which regards the maritime spectrum sensing as a classification problem. The TC2NND algorithm first classifies the received signal features by extracting cycle power spectrum (CPS) features using the FFT (Fast Fourier Transform) Accumulation Method (FAM), and then makes a judgment using a variety of C2NND decision models. The experimental results demonstrate that the proposed TC2NND algorithm could achieve a detection probability of 91.5% with a false-alarm probability of 5% at SNR = -10 dB, which significantly outperforms the conventional methods.

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