Eigenvalues of large sample covariance matrices of spiked population models
Journal of Multivariate Analysis
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks
IEEE Transactions on Wireless Communications - Part 2
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
On the condition number distribution of complex wishart matrices
IEEE Transactions on Communications
Performance of an energy detector over channels with both multipath fading and shadowing
IEEE Transactions on Wireless Communications
Fast and robust spectrum sensing via Kolmogorov-Smirnov test
IEEE Transactions on Communications
Spectrum sensing and power/rate control in CDMA cognitive radio networks
International Journal of Communication Systems
Adaptive Spectrum Sensing Algorithm in Cognitive Ultra-wideband Systems
Wireless Personal Communications: An International Journal
Volume-based method for spectrum sensing
Digital Signal Processing
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Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms.