Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Abstract

In today's wireless communication technology, spectrum occupancy is one of the major challenge. To perform all the task in wireless communication intelligently, Cognitive Radio (CR) is used. With the help of machine learning techniques, performance of CR will increase. In this paper, implementation of spectrum sensing (SS) in Cognitive Radio Network (CRN) is presented. To check the availability of spectrum, the supervised Machine Learning (ML) and conventional spectrum sensing method is used. To classify signal and noise, the Artificial Neural Network (ANN) classifier is used. The classifier's result shows better result than conventional method's result.

A Bibliometric Analysis on Spectrum Sensing in Wireless Networks

“…The null hypothesis, 𝐻 0 asserts that the frequency band is unoccupied (the licensed user is absent). Due to its minimal computational and technical complexities, energy detection is an extensively applied method for spectrum sensing [Shah and Yelalwar, 2019]. It is a detection technique for sensing the licensed user signal using the FFT (Fast Fourier transform) [Shah and Yelalwar, 2019].…”

“…Due to its minimal computational and technical complexities, energy detection is an extensively applied method for spectrum sensing [Shah and Yelalwar, 2019]. It is a detection technique for sensing the licensed user signal using the FFT (Fast Fourier transform) [Shah and Yelalwar, 2019]. The FFT converts digital signals from a time dimension to a frequency-domain form and generates the power for all frequencies, resulting in a PSD / power spectrum density.…”