Neural network-based learning schemes for cognitive radio systems
Computer Communications
Primary network cognition with spatial diversity signature
IEEE Communications Letters
Identifying spectrum usage by unknown systems using experiments in machine learning
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Computers and Electrical Engineering
EHAC'10 Proceedings of the 9th WSEAS international conference on Electronics, hardware, wireless and optical communications
On balancing exploration vs. exploitation in a cognitive engine for multi-antenna systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Cognitive engine design for link adaptation: an application to multi-antenna systems
IEEE Transactions on Wireless Communications
Enhancing a Fuzzy Logic Inference Engine through Machine Learning for a Self- Managed Network
Mobile Networks and Applications
Cognitive engine: design aspects for mobile clouds
Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
DRinK: a defense strategy of cooperative wireless terminals in a wireless multihoming environment
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Learning-Based spectrum selection in cognitive radio ad hoc networks
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Self-Organizing Maps for advanced learning in cognitive radio systems
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Wireless Personal Communications: An International Journal
Reinforcement learning in hierarchical cognitive radio wireless networks
Proceedings of the 2nd ACM workshop on High performance mobile opportunistic systems
| Hi-index | 0.00 |
Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.