Improving Spectrum Sensing for Cognitive Radio Network Using the Energy Detection with Entropy Method

Abstract

Spectrum is one of the world’s most highly regulated and limited natural resources. Cognitive Radio (CR) is a cutting-edge technology that aims to solve the future spectrum shortage issue in wireless communication systems. CR is one of the most widely used methods for maximizing the use of the wireless spectrum. Spectrum sensing is a critical step in discovering spectrum gaps in CR. Matching filter detection, energy detection (ED), cyclostationary detection, correlation coefficient detection, and wavelet detection are some of the frequency band sensing techniques. ED has received the most attention from many researchers because of its convenience and low computation complexity. However, noise instability, or the random and unavoidable variation of noise that exists in any communication link, greatly decreases the output of ED, especially whenever the signal-to-noise ratio (SNR) is poor. As a result, this research provides an exciting spectrum sensing option known as the energy detection with entropy method technique. In contrast to conventional ED, the proposed energy detection with entropy method offers better sensing performance in low SNR circumstances. According to simulation results, the proposed method has a significant performance improvement of about 18.58% when compared to CED at a given SNR of −18 dB.