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A Hybrid Signal Sensing Framework for Amateur Radio Systems Integrating Energy Detection and Advanced Waveform Classification Techniques
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Spectrum sensing
amateur radio systems
energy detection
waveform classification
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Abstract
The increasing demand for efficient spectrum utilization in amateur radio systems has necessitated the development of intelligent sensing mechanisms capable of accurately detecting and classifying signals under dynamic and uncertain environments. Traditional spectrum sensing techniques, particularly energy detection, offer simplicity but suffer from limitations such as noise uncertainty, poor performance at low signal-to-noise ratio (SNR), and inability to distinguish between signal types. Conversely, waveform classification techniques, especially those based on machine learning, provide enhanced discrimination capabilities but often incur higher computational complexity. This paper proposes a hybrid signal sensing framework that integrates energy detection with advanced waveform classification techniques to achieve robust, efficient, and scalable spectrum sensing in amateur radio environments.
The proposed framework employs a two-stage architecture wherein energy detection serves as a preliminary filtering mechanism, followed by a classification module leveraging deep learning and feature-based waveform analysis. The framework is evaluated through analytical modeling and simulation-based scenarios reflecting realistic amateur radio conditions. The results demonstrate significant improvements in detection accuracy, classification reliability, and computational efficiency compared to standalone methods. Furthermore, the framework addresses critical challenges such as noise uncertainty, fading channels, and spectrum heterogeneity. The study contributes to the advancement of intelligent radio systems by offering a balanced approach that combines low-complexity detection with high-accuracy classification.
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