Claude Shannon

Wednesday, October 25th, 2017

Room 202 in Packard Bldg., Stanford University
Parking Generally Free In Nearby Lots After 4:00 pm

Refreshments and Conversation at 6:00 P.M.
Presentation at 6:30 P.M.

Register Here

Information Theoretic Limits of Molecular Communication and System Design Using Machine Learning

Nariman Farsad, PhD
EE Department, Stanford.


    Molecular communication is a new and bio-inspired field, where chemical signals are used to transfer information instead of electromagnetic or electrical signals. In this paradigm, the transmitter releases chemicals or molecules and encodes information on some property of these signals such as their timing or concentration. The signal then propagates the medium between the transmitter and the receiver through different means such as diffusion, until it arrives at the receiver where the signal is detected and the information decoded. This new multidisciplinary field can be used for in-body communication, secrecy, networking microscale and nanoscale devices, infrastructure monitoring in smart cities and industrial complexes, as well as for underwater communications. Since these systems are fundamentally different from telecommunication systems, most techniques that have been developed over the past few decades to advance radio technology cannot be applied to them directly.
    In this talk, we first explore some of the fundamental limits of molecular communication channels, evaluate how capacity scales with respect to the number of particles released by the transmitter, and the optimal input distribution. Finally, since the underlying channel models for some molecular communication systems are unknown, we demonstrate how techniques from machine learning and deep learning can be used to design components such as detection algorithms, directly from transmission data, without any knowledge of the underlying channel models.


Photo of Nariman Farsad, PhD Nariman Farsad, PhD is currently a Postdoctoral Fellow with the Department of Electrical Engineering at Stanford University, where he is a recipient of Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship. His research interests are on emerging communication technologies such as molecular communication, and on improving the performance of communication systems through machine learning and deep learning. Nariman has won the second prize in 2014 IEEE ComSoc Student Competition: Communications Technology Changing the World, the best demo award at INFOCOM’2015, and was recognized as a finalist for the 2014 Bell Labs Prize. He has been an Area Associate Editor for IEEE Journal of Selected Areas of Communication--Special Issue on Emerging Technologies in Communications, and a Technical Reviewer for a number of journals including IEEE Transactions on Signal Processing, and IEEE Transactions on Information Theory. He was also a member of the Technical Program Committees for the ICC’2015, ICC’2018, BICT’2015, GLOBCOM’2015, GLOBCOM’2016, and GLOBECOM’2017.



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