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A Novel ANN-Based Spectrum Sensing Framework Using Energy and Statistical Features in Cognitive Radio Networks

International Journal of Electronics and Communication Engineering
© 2026 by SSRG - IJECE Journal
Volume 13 Issue 1
Year of Publication : 2026
Authors : Manshi Shah, Paresh Dholakia
10.14445/23488549/IJECE-V13I1P119
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How to Cite?

Manshi Shah, Paresh Dholakia, "A Novel ANN-Based Spectrum Sensing Framework Using Energy and Statistical Features in Cognitive Radio Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 264-274, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P119

Abstract:

Spectrum sensing is essential in cognitive radio networks to enable dynamic spectrum access and efficient utilization of spectrum resources. Conventional methods such as energy detection, matched filter-based detection, or cyclostationary-based tests approaches have limited accuracy under AWGN and Rayleigh fading, particularly at low SNR. GoF-based and hybrid schemes provide only modest gains in challenging conditions. To address this gap, an ANN-based sensing model using four features from energy and Zhang statistics of current and previous sensing windows is proposed. Experiments with an FM dataset (94.300, 96.700, and 98.300 MHz, bandwidth 0.2 MHz) acquired at 45 dB gain and a decimation rate of 64 demonstrate its effectiveness. The model achieved 86.8% accuracy and a detection probability of 0.75 at −10 dB, reaching 1.00 at +4 dB, confirming its robustness for dynamic spectrum access.

Keywords:

Artificial Neural Network, Cognitive Radio, Energy Detection, Rayleigh fading, Spectrum sensing.

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