Robust Spectrum Sensing Using Moving Blocks Energy Detector with Bootstrap

Nasrallah, Abdelhak; Hamza, Abdelkrim; Messani, Azdine

doi:10.1134/s1064226922080125

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2023

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“…Traditional SS methods include Energy Detector (ED), Matched Filter Detector (MFD) and Cyclostationary Feature Detector (CFD). Among them, ED [1,2] is the simplest technology, and does not need prior information on PU signals, and has low computational complexity. However, it is vulnerable to noise and multipath fading, resulting in poor detection performance at a low Signal Noise Ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning Spectrum Sensing Method via Geometric Power Feature

Luo

Xiao

2023

Electronics

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In order to improve the spectrum sensing (SS) performance under a low Signal Noise Ratio (SNR), this paper proposes a supervised learning spectrum sensing method based on Geometric Power (GP) feature. The GP is used as the feature vector in the supervised learning spectrum sensing method for training and testing based on the actual captured data set. Experimental results show that the detection performance of the GP-based supervised learning spectrum sensing method is better than that of the Energy Statistics (ES) and Differential Entropy (DE)-based supervised learning spectrum sensing methods.

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Section: Introductionmentioning

confidence: 99%