European Journal of Molecular & Clinical Medicine

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.

Cognitive radio is a software-based technology that provides dynamical access to unused or underused spectrum bands and enables spectrum sharing without causing any disadvantage among users. The performance of the cognitive radio in wireless communication networks depends on the accurate and fast detection of spectrum gaps.
The idea of Cognitive Radio is to share the spectrum between a so-called primary user and a so-called secondary user. The main objective of this spectrum management is to obtain a maximum rate of exploitation of the radio spectrum, for this cooperation between users is necessary. This paper provides a spectrum sensing approach using cooperation and competition to solve the spectrum allocation problem and thus ensure better management. The aim of spectrum detection is to detect spectrum gaps accurately and quickly. Therefore, the performance of cognitive radio networks largely depends on the spectrum sensing function. Particle Swarm Optimization (PSO) and Cultural Algorithm (CA) are proposed to increase spectrum detection performance in cognitive radio networks. The collaborative spectrum detection performance analysis of the proposed method was performed in Rayleigh fading channel in addition to the non-damped AWGN channel. As a result of simulation studies, it has been shown that a more effective perception can be made in cognitive radio networks by optimizing the threshold value expression from the historical data