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Guessing Random Additive Noise Decoding

GRAND offers a single, energy efficient, precise decoder for a broad swathe of codes with a small footprint, and much more.



All digital data, whether stored or communicated, can be subject to corruption that results in the received information differing from the original. In some circumstances, no additional information is available to the receiver, and that is called a hard detection situation. In others, it could be that the bits are received with a reliability measure, called the soft detection situation.

The engineering solution to this problem is to code the data. A code adds redundancy to the data, and one fruitful way of thinking about it is one takes the original data and appends a hash of it. The hash itself is, of course, potentially subject to the damaging effects of corruption, but this additional redundancy can help inform the receiver about the integrity of the data they have received. There are a wide variety of codes - that is, rules for the creation of that hash - and they were all co-designed with specific decoders that use the code-book structure, the hash structure, to try and guess what was stored or communicated based on what was received.

Most of these codes or hashes are individually only suitable for the hard or soft detection setting because they only have a hard or soft detection decoder suitable for them. Because the decoder is entirely coupled to the code structure, this typically means one needs a distinct implementation to decode each of these codes and often, when done in hardware, even distinct pieces of hardware for different levels of redundancy.

Guessing Random Additive Noise Decoding (GRAND) is a methodology based on an innovative paradigm. It is agnostic to the code structure as it aims to identify the noise effect that has impacted the data, solely using the codebook for what it is: a hash of that data.

The primary features of GRAND are:





Decodes any moderate redundancy code, regardless of structure and length, with provably maximal accuracy.

Exists for both soft and hard detection.

Is inherently highly parallelizable, resulting in desirably low latency.

Has proven energy efficient silicon implementation of hard detection. 


For more information about evaluating or licensing the GRAND™ technology and intellectual property portfolio, click here for the Technology Brief on the TLO website.

Selected Papers

Peihong Yuan, Muriel Médard, Kevin Galligan, Ken R. Duffy

arXiv 2023

Ken R. Duffy, Moritz Grundei, Muriel Médard,

IEEE Global Communications Conference (GLOBECOM) 2023

Arslan Riaz, Alperen Yasar, Furkan Ercan, Wei An, Jonathan Ngo, Kevin Galligan, Muriel Médard, Ken R. Duffy, Rabia T. Yazicigil

IEEE International Solid-State Circuits Conference (ISSCC), 2023

Ken R. Duffy, Wei An, Muriel Médard

IEEE Transactions on Signal Processing, 2022

Arslan Riaz, Vaibhav Bansal, Amit Solomon, Wei An, Qianhan Liu, Kevin Galligan, Ken R. Duffy, Muriel Médard, Rabia T. Yazicigil

IEEE European Solid-state Circuits and Devices Conference (ESSCIRC), 2021

Ken R. Duffy, Jiange Li, Muriel Médard
IEEE Transactions on Information Theory, 2019



GRAND Introduction

This brief video introduces Guessing Random Additive Noise Decoding (GRAND), a single, energy efficient, precise decoder for a broad swathe of codes with a small footprint… and much more besides.

Tutorial Slides

Universal Decoding by Guessing Random Additive Noise Decoding - GRAND

Muriel Médard, Ken R. Duffy

IEEE International Symposium on Information Theory, 2022

Code (for non-commercial purposes only)

(Non-optimized) MATLAB implementations of GRAND and ORBGRAND

Additional Links

The Network Coding and Reliable Communications Group webpage: 

The Wise Circuits Lab webpage:

MIT's Accessibility webpage:

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