EE Systems Seminar
Can machine learning trump theory in communication system design?
ABSTRACT Design and analysis of communication systems have traditionally relied on mathematical and statistical channel models that describe how a signal is corrupted during transmission. In particular, communication techniques such as modulation, coding and detection that mitigate performance degradation due to channel impairments are based on such channel models and, in some cases, instantaneous channel state information about the model. However, there are propagation environments where this approach does not work well because the underlying physical channel is too complicated, poorly understood, or rapidly time-varying. In these scenarios we propose a completely new approach to communication system design based on machine learning (ML). In this approach, the design of a particular component of the communication system (e.g. the coding strategy or the detection algorithm) utilizes tools from ML to learn and refine the design directly from training data. The training data that is used in this ML approach can be generated through models, simulations, or field measurements. We present results for three communication design problems where the ML approach results in better performance than current state-of-the-art techniques: signal detection without accurate channel state information, signal detection without a mathematical channel model, and joint source-channel coding of text. Broader application of ML to communication system design in general and to millimeter wave and molecular communication systems in particular is also discussed.
BIO Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and a professor of Electrical Engineering at Stanford University. Her research interests are in information theory, communication theory, and signal processing, and their application to wireless communications, interconnected systems, and neuroscience. She founded and served as Chief Technical Officer of Plume WiFi (formerly Accelera, Inc.) and of Quantenna (QTNA), Inc, and she currently serves on the Board of Directors for Medtronic (MDT) and Crown Castle Inc (CCI). Dr. Goldsmith is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, a Fellow of the IEEE and of Stanford, and has received several awards for her work, including the IEEE Sumner Technical Field Award, the ACM Athena Lecturer Award, the ComSoc Armstrong Technical Achievement Award, the WICE Mentoring Award, and the Silicon Valley/San Jose Business Journal’s Women of Influence Award. She is author of the book ``Wireless Communications'' and co-author of the books ``MIMO Wireless Communications'' and “Principles of Cognitive Radio,” all published by Cambridge University Press, as well as an inventor on 29 patents. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.
Dr. Goldsmith has served on the Board of Governors for both the IEEE Information Theory and Communications Societies. She served as the President of the IEEE Information Theory Society in 2009, founded and chaired the student committee of the IEEE Information Theory society, and is the founding chair of the IEEE TAB Committee on Diversity and Inclusion and the IEEE Board Committee on Diversity, Inclusion, and Ethics. At Stanford she has served as Chair of Stanford’s Faculty Senate and for multiple terms as a Senator, and on its Advisory Board, Budget Group, Committee on Research, Planning and Policy Board, Commissions on Graduate and on Undergraduate Education, Faculty Women’s Forum Steering Committee, and Task Force on Women and Leadership.
Contact: Liliana Chavarria at 626-395-4715 firstname.lastname@example.org