Communication across networks with feedback and relays is an important, challenging, and largely open problem in information and communication theory. Systematic and scalable communication algorithms will enhance reliability, decrease coding complexity or delay, impart robustness to communication schemes by exploiting diversity, and in some cases increase the achievable communication rates. Since all modern communication systems employ multiple feedback mechanisms, a better understanding of communication feedback is imperative.
This research focuses on a deeper study of this topic by viewing it through the lenses of consensus algorithms and more general stochastic approximation algorithms. This approach yields scalable and robust algorithms for cases of extreme relevance to modern communication systems, e.g., network scenarios with noisy feedback. A confluence of problems, techniques and tools from information theory and distributed dynamic systems are utilized, with a potential transformative impact on both fields. In addition to analytical techniques and simulations, the team also utilizes facilities and experience realizing novel communication algorithms in a wireless network testbed based upon software-defined radios.
Any impact on the problem of communication across networks with noisy feedback will have immediate applications to most modern communication systems. Moreover, this research furthers the unification of the two aspects and research communities relevant to a more general information theory - one that considers both the transmission of data (classical information theory), as well as its utilization (classical dynamical systems). An emphasis on organizing special sessions in conferences, offering new graduate courses, including undergraduates in the experimental research, mentoring minority students, and furthering outreach to high school students interested in engineering is an integral part of the project.
