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Maximum Eigenvalue Detection for Spectrum Sensing Under Correlated Noise
Sharma, Shree Krishna; Chatzinotas, Symeon; Ottersten, Björn
2014In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 2014
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Keywords :
Spectrum Sensing; Noise Correlation; Cognitive Radio; Random Matrix Theory
Abstract :
[en] Herein, we consider the problem of detecting primary users’ signals in the presence of noise correlation, which may arise due to imperfections in filtering and oversampling operations in a Cognitive Radio (CR) receiver. In this context, we study a Maximum Eigenvalue (ME) detection technique using recent results from Random Matrix Theory (RMT) for characterizing the distribution of the maximum eigenvalue of a class of sample covariance matrices. Subsequently, we derive a theoretical expression for a sensing threshold as a function of the probability of false alarm and evaluate the sensing performance in terms of probability of correct decision. It is shown that the proposed approach significantly improves the sensing performance of the ME detector in correlated noise scenarios.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Sharma, Shree Krishna ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Ottersten, Björn ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Language :
English
Title :
Maximum Eigenvalue Detection for Spectrum Sensing Under Correlated Noise
Publication date :
May 2014
Event name :
IEEE International Conference on Acoustics, Speech, and Signal Processing
Event organizer :
IEEE
Event place :
Florence, Italy
Event date :
4-9 May
Audience :
International
Main work title :
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 2014
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 27 May 2014

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