Topic: Can machine learning trump theory in communication system design? 

Speaker: Prof. Andrea Goldsmith, Stanford University

Date: September 13, 2018, Thursday

Time: 15:00-16:00

Venue: Governing Board Meeting Room, Dao Yuan Building

Language: English

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.

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 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.Broader application of ML to communication system design in general and to millimeter wave and molecular communication systems in particular is also discussed.

Speaker Profile:

Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and a professor of Electrical Engineering at Stanford University. Dr. Goldsmith is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, a Fellow of the IEEE. She co-founded and served as Chief Technical Officer of Plume WiFi and of Quantenna (QTNA).

She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley. She has received several awards for her work, including the IEEE Sumner Technical Field Award, the IEEE Comsoc Edwin H. Armstrong Achievement Award, the National Academy of Engineering Gilbreth Lecture Award, and the Women in Communications Engineering Mentoring Award, etc. Her research interests are in information theory and communication theory, and their application to wireless communications and related fields.