Machine Learning and Transfer Learning Based Spectrum Sensing in Cognitive Radio Networks

Emerging generations of wireless communication system require significant spectrum resources in order to deliver high data rates. Due to increase in wireless devices and applications the scarcity of spectrum occurs. Cognitive radio is the reliable and efficient solution for underutilization problem of the allocated radio spectrum. CR enables unlicensed users to find the status of authorized channel. CRN provides capability to share or use the spectrum in an opportunistic manner without causing harmful interference to Primary user. CR improves spectrum utilization and Quality of service. Spectrum sensing is the vital part in cognitive radio network. Spectrum sensing is to identify whether a specific band of frequency is being used by licensed user or not. To realize the efficient methodologies for spectrum sensing many research work have been carried out. The utmost care is mandatory for choosing the method for spectrum sensing by unlicensed Secondary users. High benefits can be achieved with the help of machine learning concepts. This survey paper provides a detailed review on machine learning and transfer knowledge centred spectrum sensing methodologies in cognitive radio networks, also research challenges and future directions for the spectrum sensing in CRN is discussed.