Self-organizing maps
Space-efficient approximate Voronoi diagrams
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
Cognitive engine implementation for wireless multicarrier transceivers
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Neural network-based learning schemes for cognitive radio systems
Computer Communications
Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks
Wireless Personal Communications: An International Journal
Review of the Self-Organizing Map (SOM) approach in water resources: Commentary
Environmental Modelling & Software
Computers and Electrical Engineering
A neural network based cognitive controller for dynamic channel selection
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Applications of Machine Learning to Cognitive Radio Networks
IEEE Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Cross layer design for equal and unequal loss protection frameworks in cognitive radio networks
Computers and Electrical Engineering
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Modern everyday life keeps making wireless communications more and more popular. The wireless communications landscape is highly varying and its success depends on the efficient provision of a physically limited natural source namely, radio spectrum. Cognitive radio systems (CRSs) have been proposed as a very promising technology for addressing this situation by facilitating more flexible and intelligent spectrum management. However, the processes of a CRS are often proved to be rather arduous and time consuming. Accordingly, a learning mechanism, capable of building knowledge to the system can speed up the whole cognition process. Framed within this statement, this paper introduces and evaluates a mechanism which is based on the well-known unsupervised learning technique, called Self-Organizing Maps (SOMs), and is used for assisting a CRS to predict the raw data rate that can be obtained, when it senses specific input data from its environment. Results show that the proposed method can provide predictions which are correct up to a percentage of 78.9% while exhibiting performance comparable to other supervised neural network-based learning schemes.