An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
OFDM for Wireless Multimedia Communications
OFDM for Wireless Multimedia Communications
IEEE Spectrum
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Estimating the entropy of a signal with applications
IEEE Transactions on Signal Processing
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Correlation-based spectrum sensing in cognitive radio
Proceedings of the 2009 ACM workshop on Cognitive radio networks
Spectral entropy based primary user detection in cognitive radio
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Chaotic characteristic based sensing for cognitive radio
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks
IEEE Communications Letters
Computers and Electrical Engineering
Wireless Personal Communications: An International Journal
A Comparative Study of Different Entropies for Spectrum Sensing Techniques
Wireless Personal Communications: An International Journal
Volume-based method for spectrum sensing
Digital Signal Processing
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In this paper, we present a simple technique for detection of primary users in cognitive radio networks with unknown noise and interference levels. We will show that the likelihood ratio test for detecting the primary user can be approximated to a formulation that compares the estimated entropy of the received signal to a suitable threshold. This formulation is also intuitive since for a given variance, the entropy of a stochastic signal is maximized if it is Gaussian. If the received signal contains the primary user's digitally modulated component, the entropy is reduced. Although the proposed approach is applicable under any scenario, we will specifically consider matched-filter-based detection in this paper, with its underlying assumption that the cognitive radio knows the primary user signaling waveform. We will consider the case where the Gaussian noise and interference levels in the region are unknown, which renders traditional matched-filtering and energy-based detection approaches unfeasible. The probabilities of successful detection and false alarm are characterized for both classical and Bayesian scenarios.