Optimization of Threshold for Energy Based Spectrum Sensing Using Differential Evolution

Narayanan, K. Aravind; Shivaram, P. Arun; Maheshkumar, M.; Santhoshkumar, K.; Kumar, L. Sathiesh; Kumar, L. Sathiesh; Narayanankutty, K.A

dc.contributor.author	Narayanan, K. Aravind
dc.contributor.author	Shivaram, P. Arun
dc.contributor.author	Maheshkumar, M.
dc.contributor.author	Santhoshkumar, K.
dc.contributor.author	Kumar, L. Sathiesh
dc.contributor.author	Kumar, L. Sathiesh
dc.contributor.author	Narayanankutty, K.A
dc.date.accessioned	2016-10-27T11:44:38Z
dc.date.available	2016-10-27T11:44:38Z
dc.date.issued	2011-07
dc.identifier.citation	Wireless Engineering and Technology, 2011, 2, 130-134	en_US
dc.identifier.uri	http://dx.doi.org/10.4236/wet.2011.23019
dc.identifier.uri	http://hdl.handle.net/123456789/1085
dc.description.abstract	Interference threshold based on energy setting in cognitive radios is a non-convex optimization problem [4]. The convergence of optimization techniques like Genetic algorithm (GA) takes several iterations to fix this threshold. Here, an attempt made to use Differential evolution (DE) method for optimization after formulating the objective functions. The advantages with this method were three fold over GA. They were, a. A reduced number of iterations, b. Marginal improvement and consistency of throughput and c. Localization of the best solution. The comparative results are presented and discussed.	en_US
dc.language.iso	en	en_US
dc.publisher	Scientific Research Publishing	en_US
dc.subject	Cognitive Radio	en_US
dc.subject	Spectrum Sensing	en_US
dc.subject	Differential Evolution	en_US
dc.title	Optimization of Threshold for Energy Based Spectrum Sensing Using Differential Evolution	en_US
dc.type	Article	en_US