A novel method for spectrum sensing in cognitive radio networks using fractional GWO[sbnd]CS optimization

Abstract

The co-channel interference and radio resources in a wireless communication system are often systematically managed by employing Radio Resource Management (RRM) strategies. The parameters of radio resources such as transmit power, data rate, error coding, and user allocation can be controlled via the RRM techniques whose main aim is to better exploit the limited radio frequency spectrum resources. The unallocated spectrum resources become spectrum holes and the effective utilization of the spectrum holes is made possible through cognitive radio by improving the network throughput. To enhance the effective energy utilization capacity of the spectrum holes by considering different sensing states, this paper integrates the grey wolf optimization (GWO) with the Cuckoo search (CS) algorithm to form the Fractional GWOCS Optimization model. The Fractional Optimization Model (FOM) allows the Secondary User (SU) for performing periodic sensing and data transfer. The main aim of the Fractional GWOCS Optimization model is to optimize different parameters like power spectral density (PSD), transmission power, and sensing bandwidth (SB). When computing energy efficiency concerning detection probability, SB, PSD, mutual optimization of PSD and SB, and convergence, the proposed methodology offers improved outcomes when compared to the existing techniques.