An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
IEEE Transactions on Wireless Communications
Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency
IEEE Communications Magazine
Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs
IEEE Communications Magazine
Multicarrier communication techniques for spectrum sensing and communication in cognitive radios
IEEE Communications Magazine
Cyclostationary Signatures in Practical Cognitive Radio Applications
IEEE Journal on Selected Areas in Communications
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We consider the spectrum sensing problem in cognitive radio networks. We offer a framework for optimal joint detection and parameter estimation when the secondary users have only a small number of signal samples. We discuss the finite-sample optimality of the generalized likelihood ratio test (GLRT) and derive the corresponding GLRT spectrum sensing algorithms by exploiting the statistics of the received signal and the prior information on the channel, noise, as well as the data signal. An iterative GLRT sensing algorithm, and a simple noniterative GLRT sensing algorithm are developed for slow and fast-fading channels, respectively, with the latter also serving as an approximate sensing method for slow-fading channels. The proposed techniques are also extended for spectrum sensing in orthogonal frequency-division multiple-access (OFDMA) systems and in multiple-input multiple-output (MIMO) systems. It is seen that the proposed simple non-iterative fast-fading GLRT sensing algorithm offers the best performance in all systems under considerations, including slow fading channels, fast fading channels, OFDMA systems, and MIMO systems, and it significantly outperforms several state-of-the-art spectrum sensing methods in these systems when there is noise uncertainty.