An Analysis of Genetic Algorithm and Tabu Search Algorithm for Channel Optimization in Cognitive AdHoc Networks

Cognitive Radio Ad Hoc Networks (CRAHNs) constitute a viable solution to solve the current problems of inefficiency in the spectrum allocation, and to deploy highly reconfigurable and self-organizing wireless networks. Cognitive Radio (CR) devices are envisaged to utilize the spectrum in an opportunistic way by dynamically accessing different licensed portions of the spectrum. However the phenomena of channel fading and primary cum secondary interference in cognitive radio networks does not guarantee application demands to be achieved continuously over time. The limited available spectrum and the inadequacy in the spectrum usage necessitate a new communication standard to utilize the existing wireless spectrum opportunistically. Here, we discuss the existing mechanisms that are followed to improve the optimization of channel allocation in cognitive network. This paper compares the techniques used to optimize the Secondary User’s performance in Channel Allocation.