Efficient Utilization of Cooperative Spectrum Sensing (CSS) in Cognitive Radio Network (CRN) Using Non-orthogonal Multiple Access (NOMA)

In the recent past, efficient cooperative spectrum sensing and usage are playing a vital role in wireless communication because of the significant progress of mobile devices. There is a recent surge and interest on non-orthogonal multiple access (NOMA) focused on communication powered by wireless mode. In modern research, more attention has been focused on efficient and accurate NOMA. NOMA wireless communication is highly adapted with cognitive radio network (CRN) for improving performance. In the existing CRN, the secondary users could be able to access the idle available spectrum while primary users are engaged. In the traditional CRN, the primary user’s frequency bands are sensed as free, the secondary users could be utilized those bands of frequency resources. In this research, the novel methodology is proposed for cooperative spectrum sensing in CRN for 5G wireless communication using NOMA. The higher cooperative spectrum efficiency can be detected in the presence of channel noise. Cooperative spectrum sensing is used to improve the efficient utilization of spectrum. The spectrum bands with license authority primary user are shared by secondary users by simultaneously transmitting information with primary users. The cooperative spectrum sensing provides well under the circumstances that the different channel interference to the primary user can be guaranteed to be negligible than an assured thresholding value. The Noisy channel state information like AWGN and Rayleigh fading channels are considered as wireless transmission mediums for transmitting a signal using multiple-input-multiple-output NOMA to increase the number of users. The proposed NOMA is fascinated with significant benefits in CRN is an essential wireless communication method for upcoming 5G technology. From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters.