Abstract

Cognitive radio appears to be a natural solution to the problems of scale and complexity resulting from the great popularity of wireless communications and the evolution of radio technologies. A cognitive radio is an intelligent agent capable of adapting to its operational context to respect the regulatory framework controlling access to the spectrum, satisfy the user's needs in terms of quality of service, and ensure optimized management of available resources (radios, networks and equipment).This new paradigm is directly linked to the development of embedded intelligence, the subject of this paper.
In this paper, we detail the design of a cognitive engine (CE) structuring the reasoning and learning operations necessary for the supervision of the dynamic reconfiguration process.
It is noted that supervised and unsupervised learning methods have been projected for various learning tasks.This paper presents ametaheuristic that is Grey Wolf Optimization (GWO) as approximate methodfor the optimization of fitness function of proposed cognitive engine. In order to establish performance evaluation of CRaccording to different criteria that we have set, such as the bit error rate (BER), output power and channel attenuation.