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An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Abhinav Shukla, Puran Gour

Abhinav Shukla and Puran Gour. An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation. International Journal of Computer Applications 162(4):7-11, March 2017. BibTeX

	author = {Abhinav Shukla and Puran Gour},
	title = {An Optimized Sensing and Detection of Cognitive Radio Network using Monte Carlo Simulation},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {4},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913258},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Cognitive Radio Network is intelligent network, which has the capability to efficiently utilize the available spectrum using various spectrum-sensing techniques, in addition with the intelligent energy consumption and bandwidth allocation. In this paper we are simulating the cognitive radio network using Monte-Carlo simulation model. The proposed system is tested under Additive White Gaussian noise (AWGN) channel and Rayleigh Fading Channel environment. During simulation the probability of detection (Pd) is calculated for given signal to noise ratio (SNR) and false alarm rate (Pf). To enhance the system performance median filter is implemented which significantly enhances the performance of detection probability for given SNR and Pf.


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Probability of Detection(Pd), False Alarm Rate(Pf), SNR, Monte-Carlo Simulation and Median Filtering.