Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
A knowledge plane for the internet
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Frequency Allocation for WLANs Using Graph Colouring Techniques
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
Cognitive engine implementation for wireless multicarrier transceivers
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
IEEE Communications Magazine
Fuzzy logic for cross-layer optimization in cognitive radio networks
IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
An autonomous cognitive access point for Wi-Fi hotspots
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Self-Organizing Maps for advanced learning in cognitive radio systems
Computers and Electrical Engineering
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In this paper, we present an application of the Cognitive Networking paradigm to the problem of dynamic channel selection in infrastructured wireless networks. We first discuss some of the key challenges associated with the cognitive control of wireless networks. Then we introduce our solution, in which a Neural Network-based cognitive engine learns how environmental measurements and the status of the network affect the performance experienced on different channels, and can therefore dynamically select the channel which is expected to yield the best performance for the mobile users. We carry out performance evaluation of the proposed system by experimental measurements on a testbed implementation; the obtained results show that the proposed cognitive engine is effective in achieving performance enhancements with respect to state-of-the-art channel selection strategies.