IEEE Future Networks World Forum
12-14 October 2022 | Montreal, Canada + Virtual (Hybrid)

TUT6: Universal Decoding by Guessing Random Additive Noise Decoding


Forward error correction decoding has traditionally been a code-specific endeavor. An innovative recent alternative is noise-centric guessing random additive noise decoding (GRAND). Our approach uses modern developments in the analysis of guesswork to create a universal algorithm where the effect of noise is guessed according to statistical knowledge of the noise behavior or through phenomenological observation. The noise effect is removed from the received signal, and the codebook is used simply as a hash check to verify whether the result is in the codebook. The guessing continues until the hash check is correct or the algorithm declares an erasure. This approach provably provides a Maximum Likelihood (ML) decoding for any block code as long as the guesswork order matches the channel statistics. The ability to incorporate statistical knowledge, such as burstiness of noise effects, such as interference or correlated fading channels, or likelihood ratios that come from soft information, can effectively guide the querying of possible noise effects. The algorithms are parallelizable and well suited to hardware implementation. Because of the universal nature of GRAND, it allows use of a variety of different codes, including techniques designed for cryptographic purposes rather than for error-correcting ones. The exploration of the use of different codes, including heretofore undecodable ones, is an interesting facet of GRAND.


Muriel Medard, Research Laboratory of Electronics, Massachusetts Institute of Technology, USA

Short Bio: Muriel Medard (Sc.D.) is the NEC Professor of Software Science and Engineering in the Electrical Engineering and Computer Science (EECS) Department at MIT She obtained three Bachelor’s degrees, as well as her M.S. and Sc.D. from MIT. She was elected president of the IEEE Information Theory Society in 2012. She is a Fellow of the Institute of Electrical and Electronics Engineers (elected 2008), a Fellow of the US National Academy of Inventors (elected 2018), the US National Academy of Engineering (elected 2020), the US Academy of Arts and Sciences (elected 2021), and a Member of Leopoldina (German Academy of Sciences) (elected 2022). She holds Honorary Doctorates from the Technical University of Munich (2020) and the University of Aalborg (2022).

Muriel has over fifty US and international patents awarded on communications technologies, the vast majority of which have been licensed or acquired. For technology transfer, she has co-founded three companies, CodeOn, for which she consults, and Steinwurf, for which she is Chief Scientist. She leads MIT’s Network Coding and Reliable Communications Group, housed in the Research Laboratory for Electronics. For more details, see:


Rabia T. Yazicigil, Dept. of ECE Boston University, USA

Short Bio: Professor Rabia Tugce Yazicigil is an Assistant Professor of Electrical and Computer Engineering Department at Boston University and a Visiting Scholar at MIT. She is leading the Wireless Integrated Systems and Extreme Circuits (WISE-Circuits) Laboratory since August 2018. She was a Postdoctoral Research Associate in the EECS Department of MIT working with Prof. Anantha P. Chandrakasan from 2016 to 2018. She received her PhD degree in Electrical Engineering from Columbia University in 2016. She received the B.S. degree in Electronics Engineering from Sabanci University, Istanbul, Turkey in 2009, and the M.S. degree in Electrical and Electronics Engineering from ´ Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland in 2011. Her research interests lie at the interface of integrated circuits, signal processing, security, bio-sensing, and wireless communications to innovate system-level solutions for future energy-constrained applications. Her recent work on cryptographic “tag of everything” was featured in MIT News in February 2020. Additionally,in 2018, her postdoctoral research work on ultra-fast bitlevel frequency hopping for physical-layer security was also featured in MIT News with additional press coverage in CNET, EurekAlert!, Engadget, and ElectronicsWeekly. com. She is the recipient of a number of awards, including the “Electrical Engineering Collaborative Research Award” for her PhD research on Compressive Sampling Applications in Rapid RF Spectrum Sensing (2016), second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015), and 2014 Millman Teaching Assistant Award of Columbia University. She has recently presented her research work in several forums as an invited keynote at the 2020 Nature Symposium focused on the Biomimetic Sensors: Their use and potential in Medicine. She recently served as the Vice Chair of the Rising Stars 2020 workshop at the IEEE International Solid-State Circuits Conference(ISSCC), and she is a member of the 2015 MIT EECS Rising Stars cohort.


Ken R. Duffy,Hamilton Institute, National University of Ireland Maynooth, Ireland

Short Bio: Ken R. Duffy (Ph.D.) is a Professor of Applied Probability at Maynooth University where he served as the Director of the Hamilton Institute, an s interdisciplinary applied mathematics research institute, between 2017 and 2022. He obtained a B.A. (mod) and Ph.D., both in mathematics from Trinity College Dublin. He is one of three co-Directors of the Science Foundation Ireland Centre for Research Training in Foundations of Data Science, which is supported by 16 enterprise alliance partners and will train over 120 Ph.D. students from 2019 to 2027.

He is a co-founder of the Royal Statistical Society’s Applied Probability Section (2011), co-authored a cover article of Trends in Cell Biology (2012), is a winner of a best paper award at the IEEE International Conference on Communications (2015), the best paper award from IEEE Transactions on Network Science and Engineering (2019), as well as the best research demo at COMSNETS (2022). For more details, see:



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