Cooperative Spectrum Sensing Performance Assessment using Machine Learning in Cognitive Radio Sensor Networks

Abstract

The Cognitive Radio (CR) is an imminent technology, intended to make more effective use of the available spectrum by giving access to licensed frequency bands by unlicensed Secondary Users (SUs) without affecting Primary licensed Users (PUs). Depending on the region where the energy is being observed, each CR communicates local decisions or the seen energy to the Fusion Center (FC). This study presents the many plots that discuss an enhanced double threshold through the Cooperative Spectrum Sensing (CSS) approach. The FC then combines local decisions with the measured energy values to reach a final decision. The usage of several machine learning methods in spectrum decision with the myopic decision is estimated. The system seeks to enhance the long-term overall performance of the SU.

Keywords

• support vector machine
• double threshold
• cognitive radio
• energy detection
• gmm
• smith-waterman algorithm
• spectrum sensing