Performance of Cooperative Spectrum Sensing Techniques in Cognitive Radio Based on Machine Learning

Due to the increasing demand for advanced wireless system applications such as long-range wireless power, vehicle-to-vehicle communication, various sensors’ inter communication, using a lot of cloud data, wireless sensing, millimeter-wave wireless, software-defined radio (SDR), etc., more and more bandwidth is required which is possible by the spectrum sensing (SS) concept in cognitive radio (CR). Therefore, researchers are showing much interest in SS techniques along with machine learning (ML), since ML has been showing optimal solutions for various computational problems. Recently, ML became an emerging technology due to its advanced applications such as product recommendations, social media features, sentiment analysis, marine wildlife preservation, predict potential heart failure, language translation, automating employee access control, image recognition, regulating healthcare efficiency and medical services, banking domain, etc. In this paper, a detailed analysis of the most recent advances is presented about cooperative spectrum sensing (CSS), cognitive radio, and ML-based CSS.