Comparative study of heuristic approaches used for spectrum sensing in cognitive radio network

Due to dynamic environment in cognitive radio network, current research rely on different machine learning techniques. Spectrum sensing is a challenging task in cognitive radio network. Finding the vacant spectrum, adjusting the transmission parameters based on the sensed spectrum and transmission of secondary user data with high throughput is a demanding task. Heuristic approaches can adjust/adapt to these tasks within less computation time. This paper gives the comparative study of various heuristic approaches suggested by different authors for spectrum sensing and spectrum allocation in cognitive radio network.