A Novel Swarm Intelligence Optimized Spectrum Sensing Approach For Cognitive Radio Network

Abstract

Spectrum sensing technique have been employed for the detection of various spectrum holes in the transmission of data for the secondary users that do not interfere with the transmission of data of the primary user. The technique known as Cognitive Radio (CR) is the one that efficiently uses the entire spectrum. The primary component of the CR is Spectrum sensing. There are certainly other factors that are considered to be important such as capabilities of cognition and awareness of sensing as well. Identified are different heuristic algorithms that are developed for solving numeric problems in optimization. As the problem has been established as NP-hard, it is essential to bring a low computation complexity heuristic solution. A greedy algorithm is used for optimizing spectrum sharing. Particle Swarm Optimization (PSO) remains an efficient and popular algorithm due to its low need for a tuning parameter, high accuracy, low time for processing, fast convergence, and simplicity. In this work, the PSO has been proposed for spectrum sensing, and this has shown better performance than the Greedy Algorithm used for the CR network spectrum sensing.