Dispersed spectrum sensing and scheduling in cognitive radio network based on SSOA-RR

Wireless communication is an emerging technology in recent days which involves the transmission of data or information over a wide range of distance. The wireless network is capable of using the unlicensed spectrum for the transmission of data for various applications like medical, science, and industries. There are cases where the licensed spectrum is not utilized up to the level. In such cases, the cognitive radio network (CRN) technology allows cognitive devices to sense it and further enables the dynamic access of the scarce resource for proper utilization. However, the excessive number of bandwidth access may lead to the occurrence of interferences among the system. This is the major issue faced in all CRNs. To resolve this, effective band scheduling mechanism in CRN has been proposed. In this research, efficient spectrum sensing is performed using stochastic optimization algorithm called Salp Swarm Optimization Algorithm (SSOA). This SSOA algorithm utilizes the best fitness function through three phases such as leaving, contention and joining of bands and provides a list of nonacquired list of bands. From the list of bands, the best band with majority bandwidth has been selected using Round Robin (RR) Algorithm. Here, the load system is allotted dynamically. Based on the load factor, the spectrum is scheduled. As a matter of fact, the whole spectrum scheduling methodology depends primarily on the base contributions made by the operation of spectrum sensing. Finally, the performance analysis is estimated for the throughput, settling time, number of bands occupied by base station (BS), and the bands occupied by each BS. From the performance analysis, the proposed system attains better results than other conventional approaches.