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How I Learned to Decode

Research seminar with Muriel Médard, Professor of Software Science and Engineering, MIT

10/20/2023
3:30 pm – 4:30 pm
Spanos Auditorium, Cummings Hall
Intended Audience(s): Public
Categories: Lectures & Seminars

Optional ZOOM LINK
Meeting ID: 994 1635 8860
Passcode: 162637

To maintain data integrity in the face of network unreliability, systems rely on error-correcting codes. System standardization, such as has been occurring for 5G, is predicated on co-designing these error-correcting codes and, most importantly, their generally complex decoders, into efficient, dedicated and customized chips.

In this talk, we show that this assumption is not necessary and has been leading to significant performance loss. We describe "Guessing Random Additive Noise Decoding," or GRAND, by Duffy, Médard and their research groups, which renders universal, optimal, code-agnostic decoding possible for low to moderate redundancy settings. GRAND enables a new exploration of codes, in and of themselves, independently of tailored decoders, over a rich family of code designs, including random ones.

For more information, contact:
Amos Johnson
amos.l.johnson@dartmouth.edu
Permanent URL to this event: https://president.dartmouth.edu/events/event?event=71153

Events are free and open to the public unless otherwise noted.