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Home | Seminars and Symposia | Past seminars/symposia: Wednesday, October 12, 2011

DTC Leading Edge Seminar Series

Highly Adaptable Cognitive Radio Networks

by

Karen Haigh
BBN Technologies
Minnesota

Wednesday, October 12, 2011
3:30 p.m. reception
4:00 p.m. seminar

401/402 Walter Library

In this talk, Dr. Haigh will describe her vision for Cognitive Networks. Recent developments in Software Defined Radio technology have created the opportunity to develop networks that are, in principle, highly adaptable and effective under a much wider range of operating conditions than currently possible, but few researchers are able to fully exploit this new flexibility. Cognitive radio networks require multiple interacting capabilities for situation assessment, planning and learning, and are therefore a rich application area for Artificial Intelligence (AI) technology. AI techniques enable real-time, context-aware adaptivity at the core of the cognitive networking vision. This presentation briefly discusses some of the AI techniques that can and have been leveraged in this domain. AI techniques are ready to be challenged with this complex real-world domain, just as networking requirements are reaching the limits of what can be done by human experts. We are at a nexus from which interesting ideas and capabilities will develop.

 

Dr. Karen Zita Haigh is a research leader in cognitive control for physical systems. Dr. Haigh has led artificial intelligence and machine learning research in MANETs, cyber security, the International Space Station, aircraft engines, and the homes of elders. She is currently the technical lead for the optimizing and learning module in a DARPA CommEx contract, where the MANET must learn how to operate under interference conditions. On DARPA's Adaptive Cognition-Enabled Radio Teams (ACERT) program, she was the lead designer of the system architecture to support rapid, asynchronous cognitive control over the global OSI stack, and also designed the cognitive control mechanism that became the first known real-world system (not simulation) to use machine learning to dynamically control MANET radio behavior. Dr. Haigh has a Ph.D. from Carnegie Mellon University, where her focus was on Machine Learning, Artificial Intelligence, and Robotics.