Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless Communications
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Entropy-based spectrum sensing in cognitive radio
Signal Processing
A Bayesian approach to spectrum sensing, denoising and anomaly detection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Bayesian inference for multiple antenna cognitive receivers
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Multiple antenna spectrum sensing in cognitive radios
IEEE Transactions on Wireless Communications
Adaptive detection in Gaussian interference with unknown covariance after reduction by invariance
IEEE Transactions on Signal Processing
GLRT-based spectrum sensing for cognitive radio with prior information
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
Multi-antenna based spectrum sensing for cognitive radios: a GLRT approach
IEEE Transactions on Communications
Wireless Communications Over Rapidly Time-Varying Channels
Wireless Communications Over Rapidly Time-Varying Channels
Parametric GLRT for Multichannel Adaptive Signal Detection
IEEE Transactions on Signal Processing
A Bayesian Approach to Adaptive Detection in Nonhomogeneous Environments
IEEE Transactions on Signal Processing
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Autoregressive modeling for fading channel simulation
IEEE Transactions on Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Detection of Rank- Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas
IEEE Transactions on Signal Processing
Hi-index | 0.08 |
Recent work on multiantenna spectrum sensing in cognitive radio (CR) networks has been based on generalized likelihood ratio test (GLRT) detectors, which lack the ability to learn from past decisions and to adapt to the continuously changing environment. To overcome this limitation, in this paper we propose a Bayesian detector capable of learning in an efficient way the posterior distributions under both hypotheses. Our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypotheses, respectively; and a binomial distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior for the channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which forms the basis of the proposed Bayesian learning procedure. The performance of the Bayesian detector is evaluated by simulations and by means of a CR testbed composed of universal radio peripheral (USRP) nodes. Both the simulations and experimental measurements show that the Bayesian detector outperforms the GLRT in a variety of scenarios.