Goodness-of-fit-based non-parametric spectrum sensing under Middleton noise for cognitive radio

A novel goodness-of-fit-based non-parametric spectrum sensing scheme in a non-Gaussian noise environment, modelled by Middleton class A distribution, is proposed. The sampling distribution of the proposed test statistic is derived and the detection performance is shown using Monte Carlo simulations....

Full description

Saved in:
Bibliographic Details
Published in:Electronics letters 2015-03, Vol.51 (5), p.419-421
Main Authors: Patel, D.K, Trivedi, Y.N
Format: Article
Language:English
Subjects:
Cognitive radio
Computer simulation
Detection
Gaussian
goodness‐of‐fit‐based nonparametric spectrum sensing
Middleton class A distribution
Middleton noise
Monte Carlo methods
Monte Carlo simulations
Noise
Non-Gaussian
nonGaussian noise environment
Sampling
sampling distribution
spread spectrum communication
statistical distributions
Statistical tests
test statistic
Wireless communications
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel goodness-of-fit-based non-parametric spectrum sensing scheme in a non-Gaussian noise environment, modelled by Middleton class A distribution, is proposed. The sampling distribution of the proposed test statistic is derived and the detection performance is shown using Monte Carlo simulations. Results are presented and it is concluded that the performance is degraded if the Gaussian component in the Middleton noise is higher than the non-Gaussian component.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2014.3780