Fast and robust spectrum sensing via Kolmogorov-Smirnov test

Authors:
Guowei Zhang;Xiaodong Wang;Ying-Chang Liang;Ju Liu
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, P.R. China;Electrical Engineering Department, Columbia University, New York, NY;Institute for Infocomm Research, A*STAR, Singapore;School of Information Science and Engineering, Shandong University, Jinan, P.R. China
Venue:
IEEE Transactions on Communications
Year:
2010

Citing 8
Cited 1

Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in Wishart matrices

IEEE Communications Letters
Eigenvalue-based spectrum sensing algorithms for cognitive radio

IEEE Transactions on Communications
Fast and robust modulation classification via Kolmogorov-Smirnov test

IEEE Transactions on Communications
Filter Bank Spectrum Sensing for Cognitive Radios

IEEE Transactions on Signal Processing
A survey of spectrum sensing algorithms for cognitive radio applications

IEEE Communications Surveys & Tutorials
Non-Gaussian noise models in signal processing for telecommunications: new methods an results for class A and class B noise models

IEEE Transactions on Information Theory
Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs

IEEE Communications Magazine
Cyclostationary Signatures in Practical Cognitive Radio Applications

IEEE Journal on Selected Areas in Communications

Application of the spectrum sensing based on the Kolmogorov - Smirnov test to the OFDM resource allocation

CSCC'11 Proceedings of the 2nd international conference on Circuits, Systems, Communications & Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new approach to spectrum sensing in cognitive radio systems based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic obtained from the received signal, and comparing it with the ECDF of the channel noise samples. A sequential version of the K-S-based spectrum sensing technique is also proposed. Extensive simulation results demonstrate that compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.