Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise
1728 - 1732
Item Usage Stats
MetadataShow full item record
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.
Gaussian mixture noise
Generalized likelihood-ratio tests
Non Gaussian channels
Gaussian noise (electronic)
Published Version (Please cite this version)http://dx.doi.org/10.1109/EUROCON.2013.6625210
Showing items related by title, author, creator and subject.
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 ...
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 ...
Alp, Y. K.; Arikan, O. (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 ...