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. She also serves on Stanford’s Presidential Advisory Board, University Budget Group, and Faculty Senate. She previously served as Chair of Stanford’s Faculty Senate and as a member of Stanford’s Commission on Graduate Education, Commission on Undergraduate Education, Committee on Research, Planning and Policy Board, and Task Force on Women and Leadership. She co-founded and served as Chief Technical Officer of Plume WiFi (formerly Accelera, Inc.) and of Quantenna (QTNA), Inc. She has also held industry positions at Maxim Technologies, Memorylink Corporation, and AT&T Bell Laboratories, and she currently chairs the Technical Advisory Boards of Interdigital Corp., Quantenna Communications, Cohere Communications, and Sequans. In the IEEE Dr. Goldsmith served on the Board of Governors for both the Information Theory and Communications societies. She has also been a Distinguished Lecturer for both societies, served as President of the IEEE Information Theory Society in 2009, founded and chaired the student committee of the IEEE Information Theory society, and chaired the Emerging Technology Committee of the IEEE Communications Society. She currently chairs the IEEE TAB committee on diversity and inclusion, and the Women in Technology Leadership Roundtable working group on metrics.
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 ComSoc Edwin H. Armstrong Achievement Award as well as Technical Achievement Awards in Communications Theory and in Wireless Communications, the National Academy of Engineering Gilbreth Lecture Award, the IEEE ComSoc and Information Theory Society Joint Paper Award, the IEEE ComSoc Best Tutorial Paper Award, the Alfred P. Sloan Fellowship, the WICE Technical Achievement 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 28 patents. Her research interests are in information theory and communication theory, and their application to wireless communications and related fields. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.
