GRAND

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.

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Background

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 an entirely new 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:

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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. 

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For further licensing information, please contact Myron Kassaraba (myronk@mit.edu) at MIT's Technology Licensing Office.

Selected Papers

Multi-code multi-rate universal maximum likelihood decoder using GRAND

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

Guessing random additive noise decoding with symbol reliability information (SRGRAND)

Ken R. Duffy, Muriel Médard, Wei An

IEEE Transactions on Communications, 2021

Ordered reliability bits guessing random additive noise decoding

Ken R. Duffy

IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2021

CRC codes as error correcting codes

Wei An, Muriel Médard, Ken R. Duffy

IEEE International Conference on Communications (ICC), 2021

Soft maximum likelihood decoding using GRAND

Amit Solomon, Ken R. Duffy, Muriel Médard

IEEE International Conference on Communications (ICC), 2020

Keep the bursts and ditch the interleavers

Wei An, Muriel Médard, Ken R. Duffy

IEEE Global Communications Conference (GLOBECOM), 2020

Capacity-achieving guessing random additive noise decoding

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

 
 

Videos

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.