Spectrum sensing measurements of pilot, energy, and collaborative detection
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
A semi-blind channel estimation method for multiuser multiantenna OFDM systems
IEEE Transactions on Signal Processing
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Design and analysis for an 802.11-based cognitive radio network
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Cross-layered design of spectrum sensing and MAC for opportunistic spectrum access
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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
Cooperative covariance and eigenvalue based detections for robust sensing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Spectrum sensing by cognitive radios at very low SNR
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Subcarrier sensing for distributed OFDMA in powerline communication
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
On the condition number distribution of complex wishart matrices
IEEE Transactions on Communications
Detection of spatially correlated Gaussian time series
IEEE Transactions on Signal Processing
A Bayesian framework for collaborative multi-source signal sensing
IEEE Transactions on Signal Processing
Adaptive joint scheduling of spectrum sensing and data transmission in cognitive radio networks
IEEE Transactions on Communications
On marginal distributions of the ordered eigenvalues of certain random matrices
EURASIP Journal on Advances in Signal Processing
Fast and robust spectrum sensing via Kolmogorov-Smirnov test
IEEE Transactions on Communications
Spectrum Sensing Framework for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
A sub-space method to detect multiple wireless microphone signals in TV band white space
Analog Integrated Circuits and Signal Processing
A bispectrum detector for FM modulated wireless microphone signals
Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
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
Spectrum-efficient cognitive radio transceiver using multiwavelet filters
ISRN Communications and Networking
A reconfigurable upper audio band modem for data communication between mobile devices
Analog Integrated Circuits and Signal Processing
A real-time, low-power implementation for high-resolution eigenvalue-based spectrum sensing
Analog Integrated Circuits and Signal Processing
Volume-based method for spectrum sensing
Digital Signal Processing
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Spectrum sensing is a fundamental component in a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.