Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Learning Bayesian Networks
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A Context Driven Architecture for Cognitive Radio Nodes
Wireless Personal Communications: An International Journal
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IEEE Transactions on Mobile Computing
Modelling user preferences and configuring services in B3G devices
Wireless Networks
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A comparison of pilot-aided channel estimation methods for OFDMsystems
IEEE Transactions on Signal Processing
Subspace-based (semi-) blind channel estimation for block precodedspace-time OFDM
IEEE Transactions on Signal Processing
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IEEE Transactions on Signal Processing
Blind OFDM channel estimation through simple linear precoding
IEEE Transactions on Wireless Communications
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IEEE Communications Magazine
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IEEE Journal on Selected Areas in Communications
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Utility-Aware Cognitive Network Selections in Wireless Infrastructures
Wireless Personal Communications: An International Journal
Self-Organizing Maps for advanced learning in cognitive radio systems
Computers and Electrical Engineering
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This paper proposes enhancements to the channel(-state) estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.