My understanding of GRAND is that it relies on the fact that the SNR is high -- which is very much not the case for FT8.
Philip On Tue, Sep 21, 2021 at 3:26 PM Glenn M-H via wsjt-devel < wsjt-devel@lists.sourceforge.net> wrote: > Hi! > > Please consider using "Guessing Random Additive Noise Decoding" (GRAND) > when decoding whatever digital modes. > > - > > Expect that all existing ECC-software can be replaced by this (universal) > ECC-software (or hardware if used); "Guessing Random Additive Noise > Decoding" (GRAND) - article mentioning: > > September 14, 2021, New Chip [but also as software] Can Decode Any Type of > Data Sent Across a Network: > > https://scitechdaily.com/new-chip-can-decode-any-type-of-data-sent-across-a-network/ > Citat: "... > Since the 1950s, most error-correcting codes and decoding algorithms have > been designed together. Each code had a structure that corresponded with a > particular, highly complex decoding algorithm, which often required the use > of dedicated hardware. > *Researchers at MIT, Boston University, and Maynooth University in Ireland > have now created the first silicon chip that is able to decode any code, > regardless of its structure, with maximum accuracy, using a universal > decoding algorithm called Guessing Random Additive Noise Decoding (GRAND). > By eliminating the need for multiple, computationally complex decoders, > GRAND enables increased efficiency that could have applications in > augmented and virtual reality, gaming, 5G networks, and connected devices > that rely on processing a high volume of data with minimal delay.* > ... > One way to think of these codes is as redundant hashes (in this case, a > series of 1s and 0s) added to the end of the original data. The rules for > the creation of that hash are stored in a specific codebook. > As the encoded data travel over a network, they are affected by noise, or > energy that disrupts the signal, which is often generated by other > electronic devices. When that coded data and the noise that affected them > arrive at their destination, the decoding algorithm consults its codebook > and uses the structure of the hash to guess what the stored information is. > Instead, GRAND works by guessing the noise that affected the message, and > uses the noise pattern to deduce the original information. GRAND generates > a series of noise sequences in the order they are likely to occur, > subtracts them from the received data, and checks to see if the resulting > codeword is in a codebook. > ... > The GRAND chip uses a three-tiered structure, starting with the simplest > possible solutions in the first stage and working up to longer and more > complex noise patterns in the two subsequent stages. Each stage operates > independently, which increases the throughput of the system and saves power. > The device is also designed to switch seamlessly between two codebooks. It > contains two static random-access memory chips, one that can crack > codewords, while the other loads a new codebook and then switches to > decoding without any downtime. > *The researchers tested the GRAND chip and found it could effectively > decode any moderate redundancy code up to 128 bits in length, with only > about a microsecond of latency.* > Médard and her collaborators had previously demonstrated the success of > the algorithm, but this new work showcases the effectiveness and efficiency > of GRAND in hardware for the first time. > ... > *Since GRAND only uses codebooks for verification, the chip not only works > with legacy codes but could also be used with codes that haven’t even been > introduced yet.* > …" > > September 9 2021, A universal system for decoding any type of data sent > across a network. > New chip eliminates the need for specific decoding hardware, could boost > efficiency of gaming systems, 5G networks, the internet of things, and more: > https://news.mit.edu/2021/grand-decoding-data-0909 > > https://www.granddecoder.mit.edu/ > > Muriel Medard: > https://en.wikipedia.org/wiki/Muriel_M%C3%A9dard > > ECC incl. FEC: > https://en.wikipedia.org/wiki/Error_correction_code > > - > > 1 time 19 min: > 23. maj 2021, It’s All in the Noise: Universal Noise-Centric Decoding | > IEEE Montreal Keynote Event: > https://www.youtube.com/watch?v=QbPfylinEXU > > 1 time video: > 7. sep. 2020, Prof. Muriel Médard - Guessing Random Additive Noise > Decoding (GRAND): > https://www.youtube.com/watch?v=IWVQl_Bn4gE > > FIU SCIS Distinguished Lecture Series: Guessing Random Additive Noise > Decoding(GRAND)- Muriel Medard: > https://www.youtube.com/watch?v=6iRCbu-Iozw > > 23. okt. 2020, Muriel Medard: Guessing Random Additive Noise Decoding > (GRAND) (Keynote Address): > https://www.youtube.com/watch?v=WA1JmV7hhPw > > Reliable Communication over Noisy Channels by Guessing Random Additive > Noise Decoding (GRAND): > https://www.youtube.com/watch?v=U1QlmXqYN1A > > - > > Articles: > > 23 Aug 2021, Guessing random additive noise decoding with symbol > reliability information (SRGRAND): > https://arxiv.org/abs/1902.03796 > > 22 Mar 2019, Capacity-achieving Guessing Random Additive Noise Decoding > (GRAND): > https://arxiv.org/abs/1802.07010 > > IEEE Transactions on Information Theory ( Volume: 65, Issue: 7, July 2019 > pp. 4023-4040): "Capacity-Achieving Guessing Random Additive Noise > Decoding”: > https://ieeexplore.ieee.org/document/8630851 > > GRAND Duffy, Li & Medard, IEEE ISIT, 2018; IEEE Trans Inf Theory, 2019, > An, Medard, Duffy, preprint, 2020. > > SRGRAND Duffy & Medard, IEEE ISIT, 2019; Duffy, Solomon, Konwar & Medard, > CISS, 2020; Duffy, Medard & An, arXiv: 1902.03796; > https://arxiv.org/abs/1902.03796 > > ORBGRAND Duffy, arXiv:2001.00546, to appear ICASSP 2021; > https://arxiv.org/abs/2001.00546 > > SGRAND Solomon, Duffy & Medard, IEEE ICC, 2020. > > -- > best regards, > > Glenn, OZ1HFT > _______________________________________________ > wsjt-devel mailing list > wsjt-devel@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/wsjt-devel >
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