A Comprehensive Survey on Effective Spectrum Sensing in 5G Wireless Networks through Cognitive Radio Networks

Abstract

Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband spectrum sensing gains more attention due to emerging 5G wireless networks characterized by high data rates in the order of hundreds of Gbps. Conventional sensing techniques uses samples for its observations based on Nyquist rate. Due to hardware cost and sampling rate limitations those techniques can sense only one band at a time. In spite of this issue secondary users have to sense multiple frequency bands using frontend technologies which leads into increased cost, time and complexity. Considering the facts and issues, compressive sensing was introduced to minimize the computation time by improving the sensing process even for high dimensional resources. Holding the essential information and reduces the sample size which is related to high dimensional data acquisition is performed in compressive sensing. In the last decade various researchers paid more attention to improve the performance of compressive sensing in cognitive radio networks based on sensing matrix, sparse representation and recovery process. This survey paper provides an in-depth analysis of conventional models and its sensing strategies in cognitive radio networks along with its merits and demerits to obtain a detailed insight about compressive sensing.