The objective of this research is to develop the theoretical foundations for understanding implicit and explicit communication within cyber-physical systems. The approach is two-fold: (a) developing new information-theoretic tools to reveal the essential nature of implicit communication in a manner analogous to (and compatible with) classical network information theory; (b) viewing the wireless ecosystem itself as a cyber-physical system in which spectrum is the physical substrate that is manipulated by heterogeneous interacting cyber-systems that must be certified to meet safety and performance objectives. The intellectual merit of this project comes from the transformative technical approaches being developed. The key to understanding implicit communication is a conceptual breakthrough in attacking the unsolved 40-year-old Witsenhausen counterexample by using an approximate-optimality paradigm combined with new ideas from sphere-packing and cognitive radio channels. These techniques open up radically new mathematical avenues to attack distributed-control problems that have long been considered fundamentally intractable. They guide the development of nonlinear control strategies that are provably orders-of-magnitude better than the best linear strategies. The keys to understanding explicit communication in cyber-physical systems are new approaches to active learning, detection, and estimation in distributed environments that combine worst-case and probabilistic elements. Beyond the many diverse applications (the Internet, the smart grid, intelligent transportation, etc.) of heterogeneous cyber-physical systems themselves, this research reaches out to wireless policy: allowing the principled formulation of government regulations for next-generation networks. Graduate students (including female ones) and postdoctoral scholars will be trained and research results incorporated into both the undergraduate and graduate curricula.
Performance Period: 09/15/2009 - 08/31/2013
Institution: University of California-Berkeley
Sponsor: National Science Foundation
Award Number: 0932410