Supervised Learning Spectrum Sensing Method via Geometric Power Feature

Abstract

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.

A Review of Research on Spectrum Sensing Based on Deep Learning

“…Shreeram Suresh Chandra et al conducted experiments [65] on several methods based on energy, differential entropy [66], geometric power [67], and the P-paradigm [68] using neural network structures such as DNNs, CNNs, ResNet, MLPs, etc., in order to compare the performances and study the optimal combinations of neural networks with signal processing methods. The experiments in this paper demonstrate that as the depth of a CNN increases, its performance also increases, but the vanishing gradient problem also occurs, so residual blocks are introduced to solve this problem.…”