Adaptive signal processing
Practical neural network recipes in C++
Practical neural network recipes in C++
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
EURASIP Journal on Applied Signal Processing
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Managing the Radio Spectrum: Hands-On or Back-Off?
IT Professional
Applications of Machine Learning to Cognitive Radio Networks
IEEE Wireless Communications
Dynamic spectrum allocation in composite reconfigurable wireless networks
IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Management System for Terminals in the Wireless B3G World
Wireless Personal Communications: An International Journal
Telematics and Informatics
Computers and Electrical Engineering
Context Matching for Realizing Cognitive Wireless Network Segments
Wireless Personal Communications: An International Journal
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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Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the ''cognition cycle'', during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms that are capable of exploiting measurements sensed from the environment, gathered experience and stored knowledge, are judged as rather beneficial for guiding decisions and actions. Framed within this statement, this paper introduces and evaluates learning schemes that are based on artificial neural networks and can be used for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration. In particular, the focus in this work is placed on obtaining insight on the behavior of the presented, learning schemes, whereas useful, indicative results from the benchmarking work, conducted in order to design and use an appropriate neural network structure, are also presented and discussed. In the near future, such learning schemes are expected to assist a cognitive radio system to compare among the whole of available, candidate radio configurations and finally select the best one to operate in.