Neural network-based learning schemes for cognitive radio systems
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A cognitive radio (CR) is a transceiver that is aware, adaptive, and capable of learning. It may be visualized and realized as an intelligent software package (cognitive engine) controlling a software defined radio platform. The cognitive engine executes a set of nested loops constituting a cognition cycle, drawing on experience and stored knowledge to optimize a set of user-chosen quality of service measures. The process relies on the effectiveness of a set of tools that are individually well known but rarely found together: multi-objective genetic algorithms, case-based decision theory, and neural networks. Practical implementation problems include passing environmental information from the radio to the cognitive engine, acting on that information, and performing real-time control of the radio platform by the cognitive engine. In this paper, we discuss our approach to developing and implementing a CR. Copyright © 2007 John Wiley & Sons, Ltd. Dr. Mitola's affiliation with The MITRE Corporation is provided for identification purposes only and does not imply the support of MITRE nor its sponsors for this publication nor the views expressed.