Spectrum allocation using genetic algorithm in cognitive radio networks

Cognitive radio allows unlicensed operation in licensed bands to meet the growing demand for radio spectrum. The intelligent device, in a spectral environment, must detect free frequencies to create a cognitive network without interfering licensed users. In this paper, we are interested in finding the optimal spectrum to be allocated to users in cognitive radio networks by ensuring quality of service satisfying. The problem is started by studying the possibility of using genetic algorithm (GA) to accommodate the secondary users in best possible space in the spectrum by interacting with the dynamic radio environment. The main advantage of GA over other soft computing techniques is its multi-objective handling capability. The cognitive radio will sense the radio frequency parameter from the environment and the reasoning engine in the cognitive radio will take the required decisions in order to provide new spectrum allocation as demanded by the user. GA defines a radio in the form of chromosomes and genes and the user's quality of service needs are given as input to the GA procedure. We analyze the impact of both parameters, available spectrum resources size which is defined by the GA as a population size, and the number of defined chromosome's genes in the efficiency of spectrum allocation. The performance analysis results are showed in Matlab.