Skip navigation

Robust spectrum sensing based on statistical tests

Robust spectrum sensing based on statistical tests

Arshad, Kamran, Briggs, Keith and Moessner, Klaus (2011) Robust spectrum sensing based on statistical tests. In: Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum. ACM, New York, NY, USA. ISBN 9781450309127 (doi:10.1145/2093256.2093268)

Full text not available from this repository.

Abstract

Spectrum sensing, in particular, detecting the presence of incumbent users in licensed spectrum, is one of the pivotal task for cognitive radios (CRs). In this paper, we provide solutions to the spectrum sensing problem by using statistical test theory, and thus derive novel spectrum sensing approaches. We apply the classical Kolmogorov-Smirnov (KS) test to the problem of spectrum sensing under the assumption that the noise probability distribution is known. In practice, the exact noise distribution is unknown, so a sensing method for Gaussian noise with unknown noise power is proposed. Next it is shown that the proposed sensing scheme is asymptotically robust and can be applied to non-Gaussian noise distributions. We compare the performance of sensing algorithms with the well-known Energy Detector (ED) and Anderson-Darling (AD) sensing proposed in recent literature. Our paper shows that proposed sensing methods outperform both ED and AD based sensing especially for the most important case when the received Signal to Noise Ratio (SNR) is low.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum
Additional Information: [1] This paper was first presented at the 4th International Conference on Cognitive Radio and Advanced Spectrum Management (CogART '11) held from 26-29 October 2011 in Barcelona, Spain. [2] Article no. 12.
Uncontrolled Keywords: spectrum sensing, statistics
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Engineering
School of Engineering > Mobile & Wireless Communications Research Laboratory
Related URLs:
Last Modified: 14 Oct 2016 09:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/11320

Actions (login required)

View Item View Item