Research Article
Interference Mitigation in Cognitive Radio using Genetic Algorithm
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 70 - Issue 12 |
| Published: May 2013 |
| Authors: Soma Chakraborty, Rashmi Deka, Jibendu Sekhar Roy |
10.5120/12016-8059
|
Soma Chakraborty, Rashmi Deka, Jibendu Sekhar Roy . Interference Mitigation in Cognitive Radio using Genetic Algorithm. International Journal of Computer Applications. 70, 12 (May 2013), 29-35. DOI=10.5120/12016-8059
@article{ 10.5120/12016-8059,
author = { Soma Chakraborty,Rashmi Deka,Jibendu Sekhar Roy },
title = { Interference Mitigation in Cognitive Radio using Genetic Algorithm },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 70 },
number = { 12 },
pages = { 29-35 },
doi = { 10.5120/12016-8059 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Soma Chakraborty
%A Rashmi Deka
%A Jibendu Sekhar Roy
%T Interference Mitigation in Cognitive Radio using Genetic Algorithm%T
%J International Journal of Computer Applications
%V 70
%N 12
%P 29-35
%R 10.5120/12016-8059
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In order to improve the spectrum utilization, cognitive networks have been proposed. A cognitive network can reuses the spectrum of licensed user in a way such that the services of the licensed users are not disrupted harmfully. This paper presents the optimization of interference generated by a secondary network to a primary network for a cognitive radio (CR) networks using genetic algorithm (GA). The interference model used for optimization, in cognitive radio networks, is presented employing power control. A power control scheme is studied to govern the transmission power of a CR node. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first studied numerically and then under the control scheme the interference distributions are fitted by log-normal distributions with reduced complexity. In GA optimization, the chromosome's genes correspond to the adjustable parameters in a given radio, and the chromosomes are genetically manipulated so that GA can find a set of parameters that optimize the radio according to the user's current needs.
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