Robust Inference and Communication: Theory, Algorithms and Performance Analysis

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Robust Inference and Communication: Theory, Algorithms and Performance Analysis Sean P. Meyn and Venugopal V. VeeravalliAs sensors and wireless communication become increasingly pervasive, and network topologies increasingly complex, there is an urgent need for new techniques for inference and communication in complex environments, as well as techniques to evaluate their performance. The goal of this investigation is to respond to these needs by following two complementary tracks. The first concerns methods for constructing inference and decoding algorithms for complex models, with possible modeling uncertainty, based on the geometry surrounding Kullback-Leibler (K-L) divergence and related methods developed by the investigators in their prior research. The second track treats performance evaluation and performance improvement. Both performance evaluation and algorithm selection are performed using Monte-Carlo or related sample path learning techniques. These approaches are chosen primarily because of their ease of application when compared to deterministic numerical techniques. The application of Monte-Carlo techniques comes at a price in the form of high variance. Efficient simulation and learning techniques are developed in concert with research on hypothesis testing and communication to construct faster algorithms for performance evaluation and adaptation. In addition to theoretical research on these topics, the investigators will transfer technology to industry and community organizations, including the Motorola Communications Center at Illinois, United Technologies Research Center, Vodafone, and the community wireless group CUWiN. Both graduate and undergraduate students will be engaged in applied and theoretical research.