Deep learning-driven compressed sensing using U-Net for cognitive radio spectrum detection

Kavitha Veerappan; G. Seetharaman

doi:10.1007/s11276-026-04087-6

February 3, 2026Wireless Networks

Deep learning-driven compressed sensing using U-Net for cognitive radio spectrum detection

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Key Points

Enhanced spectrum detection accuracy achieved using the U-Net architecture and deep learning techniques, improving cognitive radio performance.
Analyzed data shows significant improvements, with over 90% detection accuracy in noisy environments during multiple trials.
Methodology involved employing a deep learning framework based on compressed sensing to process complex spectrum data efficiently.
These findings suggest that incorporating deep learning into radio spectrum detection can enhance system capabilities, though real-world applications need further exploration.

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Implication

Observational analysis demonstrates enhanced spectrum detection in cognitive radio systems, indicating improved efficiency through deep learning techniques.

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Veerappan et al. (2026) studied this question.

synapsesocial.com/papers/69a76676badf0bb9e87dd18b https://doi.org/10.1007/s11276-026-04087-6

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Deep learning-driven compressed sensing using U-Net for cognitive radio spectrum detection

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Deep learning-driven compressed sensing using U-Net for cognitive radio spectrum detection

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