The 13th IEEE/IET International Symposium on Communications Systems, Networks and Digital Signal Processing (CSNDSP) will be held in Porto, Portugal from July 20-July 22, 2022. CSNDSP is celebrating 25 years! First started in 1998, CSNDSP has been running biennially and grown to attract around 200 delegates from across the globe to present and discuss research in the fields of communication systems and networks, digital signal processing, and related areas.
On Day 2, July 21st, Muriel Médard will be giving Plenary Talk 03 from 9:00AM-10:00AM in Auditorium II. The title of the talk is “Guessing Random Additive Noise Decoding (GRAND) or how to stop worrying about error-correcting code design.”
Abstract: 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 is 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. Moreover, recent work with Yazicigil and her group has demonstrated that such decoding can be implemented with extremely low latency in silicon. 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. Surprisingly, even the simplest code constructions, such as those used merely for error checking, match or markedly outperform state of the art codes when optimally decoded with GRAND. Without the need for highly tailored codes and bespoke decoders, we can envisage using GRAND to avoid the issue of limited and sub-optimal code choices that 5G encountered, and instead have an open platform for coding and decoding.
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