Entropy-based spectrum sensing in cognitive radio
Signal Processing
Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in Wishart matrices
IEEE Communications Letters
Eigenvalue-based spectrum sensing algorithms for cognitive radio
IEEE Transactions on Communications
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
Autocorrelation-based decentralized sequential detection of OFDM signals in cognitive radios
IEEE Transactions on Signal Processing
Collaborative cyclostationary spectrum sensing for cognitive radio systems
IEEE Transactions on Signal Processing
Correlation matching approach for spectrum sensing in open spectrum communications
IEEE Transactions on Signal Processing
Multiple antenna spectrum sensing in cognitive radios
IEEE Transactions on Wireless Communications
Advanced detection techniques for cognitive radio
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
Cramer-Rao bounds of DOA estimates for BPSK and QPSK Modulated signals
IEEE Transactions on Signal Processing
Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks
IEEE Transactions on Wireless Communications
On the capacity of spatially correlated MIMO Rayleigh-fading channels
IEEE Transactions on Information Theory
A space-time correlation model for multielement antenna systems in mobile fading channels
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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It is recently shown that algorithms derived from random matrix theory (RMT) can provide superior performance for spectrum sensing, which corresponds to the task of detecting the presence of primary users in cognitive radio. The essence of the RMT-based methods is to utilize the distribution of extremal eigenvalues of the received signal sample covariance matrix (SCM), namely, the Tracy-Widom (TW) distribution. Although the TW distribution is quite useful in spectrum sensing, computationally demanding numerical evaluation is required because it does not have an explicit closed-form expression. In this paper, we devise two novel volume-based detectors by exploiting the determinant of the SCM or volume to distinguish between the signal-presence and signal-absence cases. With the use of RMT, we accurately produce the theoretical decision threshold for one of the detectors under the Gaussian noise assumption. Simulation results are included to illustrate the effectiveness of the volume-based detectors.