Optimization the Cognitive Radio Decision Based on Improved Nature Inspired Approach

Abstract

Cognitive radio networks (CRNs) are group of nodes prepared through cognitive radios which can improve the performance automatically via changing its behavior to adapt to the environment. Although various routing protocols that reflect the CRNs decisions are proposed to incorporate the varying degrees of adaptation, these protocols are to provide QoS guarantees to the CRNs. In order to effectively transmit data packets and to develop cognitive routing protocols and to enforce challenges as a result of the changing nature of the obtainable spectrum, this research proposes an optimization path routing algorithm depend on the basis of the one of inspired approaches (Specifically improved cat swarm algorithm) to speed up the exploration for the optimal routes. This algorithm is a new met heuristic algorithm. It is being used for solving optimization problem. This paper presents an advanced routing algorithm specified for this networks, this algorithm is based on the cat swarm algorithm, makes the transmission process effectual, adaptive in addition to scalable with an cumulative number of nodes. The new guideline proposed better satisfies the demands of QoS and show that the algorithm is valid and effective in controlling the packet loss ratio, time delay and the residual bandwidth while satisfying service requirements, emphasizing of some important characteristics of cat swarm search algorithm. Finally simulation results illustration that the proposed work offer efficient bandwidth exploitation.