Spectrum Sensing Mechanism For Congnitive Radio using Deep Learning

Abstract

An interesting modern technology called cognitive radio creates new opportunities for the effective utilization of the spectrum. Deep Learning (DL) techniques rely on experimentally recorded data and, when trained properly with a wide range of data, may effectively recognize the radio settings, adapt to different environments, and constantly provide a great performance. Using a variety of signal processing (SP) features, we compare the performance of various deep neural network (DNN) models for spectrum sensing (SS) in this paper. The features that are taken into consideration are differential entropy, energy, Lp-norm and geometric power. Conventional DNN are trained to perform spectrum sensing (SS) in congnitive radio (CR) with two different models of noise. In one noise model we take experimentally recorded data from an unoccupied frequency modulation broadcast channel and in another noise model we consider generalized Gaussian noise (GGN). Through thorough tests based on real-world collected datasets, we find that ResNet and Multilayer perceptron (MLP) architectures provide the most effective result in perspective of likelihood of detection of primary user, for a specific preset value of false-alarm probability.