Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise
Author
Turgut, Esma
Gezici, Sinan
Date
2013Source Title
Eurocon 2013
Publisher
IEEE
Pages
1728 - 1732
Language
English
Type
Conference PaperItem Usage Stats
194
views
views
160
downloads
downloads
Abstract
In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under constraints on the worst-case detection and false-alarm probabilities. We derive a restricted NP based spectrum sensing algorithm for additive Gaussian mixture noise channels, and compare its performance against the generalized likelihood ratio test (GLRT) and the energy detector. Simulation results show that the proposed spectrum sensing algorithm provides improvements over the other approaches in terms of minimum (worst-case) and/or average detection probabilities. © 2013 IEEE.
Keywords
Cognitive radioDetection
Gaussian mixture
Likelihood ratio
Neyman-Pearson
Spectrum sensing
Detection probabilities
Gaussian mixture noise
Gaussian mixtures
Generalized likelihood-ratio tests
Likelihood ratios
Neyman-pearson
Non Gaussian channels
Spectrum sensing
Algorithms
Cognitive radio
Error detection
Probability
Radio systems
Telecommunication
Gaussian noise (electronic)
Permalink
http://hdl.handle.net/11693/27897Published Version (Please cite this version)
http://dx.doi.org/10.1109/EUROCON.2013.6625210Collections
Related items
Showing items related by title, author, creator and subject.
-
Performance analysis of turbo codes over Rician fading channels with impulsive noise
Ali, Syed Amjad; Ince, E.A. (IEEE, 2007)The statistical characteristics of impulsive noise differ greatly from those of Gaussian noise. Hence, the performance of conventional decoders, optimized for additive white Gaussian noise (AWGN) channels is not promising ... -
Image histogram thresholding using Gaussian kernel density estimation (English)
Suhre, Alexander; Çetin, A. Enis (IEEE, 2013)In this article, image histogram thresholding is carried out using the likelihood of a mixture of Gaussians. In the proposed approach, a prob ability density function (PDF) of the histogram is computed using Gaussian kernel ... -
Time-frequency analysis of signals using support adaptive Hermite-Gaussian expansions
Alp, Y. K.; Arıkan, Orhan (Elsevier, 2012-05-18)Since Hermite-Gaussian (HG) functions provide an orthonormal basis with the most compact time-frequency supports (TFSs), they are ideally suited for time-frequency component analysis of finite energy signals. For a signal ...