Cooperative covariance and eigenvalue based detections for robust sensing

Authors:
Yonghong Zeng;Ying-Chang Liang;Edward C. Y. Peh;Anh Tuan Hoang
Affiliations:
A*STAR, Singapore;A*STAR, Singapore;A*STAR, Singapore;A*STAR, Singapore
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 4
Cited 3

Distributed Detection and Data Fusion

Distributed Detection and Data Fusion
Eigenvalue-based spectrum sensing algorithms for cognitive radio

IEEE Transactions on Communications
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks

IEEE Transactions on Wireless Communications
Sensing-Throughput Tradeoff for Cognitive Radio Networks

IEEE Transactions on Wireless Communications

A review on spectrum sensing for cognitive radio: challenges and solutions

EURASIP Journal on Advances in Signal Processing - Special issue on advanced signal processing for cognitive radio networks
Fuzzy likelihood ratio test for cooperative spectrum sensing in cognitive radio

Signal Processing
Cooperative spectrum sensing against noise uncertainty using Neyman-Pearson lemma on fuzzy hypothesis test

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their robustness at very low signal-to-noise ratio (SNR) and uncertain noise/interference environment is generally not satisfactory. In this paper, the concept of robust sensing is discussed first. Subsequently the cooperative covariance and eigenvalue based detections are proposed for robust spectrum sensing. It is proved mathematically that under some conditions the proposed methods are robust to uncertain and unpredictable noise and interference. The performances of the methods are also verified by simulations.