Composite hypothesis testing for cooperative spectrum sensing in cognitive radio

Authors:
Sepideh Zarrin;Teng Joon Lim
Affiliations:
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada;Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

Citing 3
Cited 4

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory
Cognitive radio: brain-empowered wireless communications

IEEE Journal on Selected Areas in Communications

Cooperative quickest spectrum sensing in cognitive radios with unknown parameters

GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Cooperative spectrum sensing in cognitive radios with incomplete likelihood functions

IEEE Transactions on Signal Processing
Cooperative spectrum sensing in cognitive radio networks: A survey

Physical Communication
Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a composite hypothesis testing approach for cooperative spectrum sensing. We derive the optimal likelihood ratio test (LRT) statistic based on the Neyman-Pearson (NP) criterion at the fusion center for both hard (one-bit) and quantized (multi-bit) local decisions. We show that the LRT statistic depends on the modulation type and second-and fourth- order statistics of the primary signal. However, such side information is not commonly available to the secondary network. Therefore, we propose to apply composite hypothesis testing methods, such as the Rao test, which do not require any prior knowledge about the primary signal, in a cooperative sensing scenario. We derive a modified Rao test statistic for decision making at the fusion center for both cases of hard and quantized local decisions. We also apply the locally most powerful (LMP) detector at the fusion center for weak primary signals and derive its corresponding test statistic. These methods are much simpler than the optimal NP-based method and do not require estimation of the primary signal statistics while having a very close performance to the optimal method.