BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Eastern BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210515T173142Z UID:72CFAF82-DD21-4F52-9C3D-7434C9FCB3EC DTSTART;TZID=Canada/Eastern:20210513T180000 DTEND;TZID=Canada/Eastern:20210513T200000 DESCRIPTION:Please join us for this year IEEE Montréal Keynote Event:\n\nC laude Shannon's 1948 "A Mathematical Theory of Communication" provided the basis for the digital communication revolution. As part of that ground-br eaking work\, he identified the greatest rate (capacity) at which data can be communicated over a noisy channel. His proposed algorithm used on rand om codes and a code centric maximum Maximum Likelihood (ML) decoding\, whe re channel outputs are compared to all possible input codewords to select the most likely candidate based on the observed channel output. Despite it s mathematical elegance\, his code centric decoding algorithm is impractic al from a complexity perspective and much work in the intervening 70 years has focused on co-designing codes and decoders that enable reliable commu nication at high rates.\n\nIn collaboration with Ken Duffy and his group\, we introduce a new algorithm\, Guessing Random Additive Noise Deceasing ( GRAND) for a noise-centric\, rather than code-centric\, ML decoding. The r eceiver rank orders noise effect sequences from most likely to least likel y\, and guesses accordingly. When inverting\, in decreasing order of likel ihood\, noise effect sequences from the received signal\, the first instan ce that results in an element of the code-book is the ML decoding. Our res ults show that\, with GRAND\, even extremely simple codes\, such as CRCs\, match or outperform state of the art code/decoder pairs\, indicating that the choice of decoder is likely to be more important than that of code.\n \nWe illustrate the practical usefulness of our approach and discuss its h ardware implementation\, done with Rabia Yazicigil and her group. The comp lexity of the decoding is\, for the sorts of channels generally used in co mmercial applications\, quite low\, unlike code-centric ML and the chip is able to decode any linear code.\n\nSpeaker(s): Professor Muriel Medard\, \n\nVirtual: https://events.vtools.ieee.org/m/268217 LOCATION:Virtual: https://events.vtools.ieee.org/m/268217 ORGANIZER:ieee.mtl.section@gmail.com SEQUENCE:6 SUMMARY:IEEE Montreal Keynote Event- It's all in the noise - universal nois e-centric decoding URL;VALUE=URI:https://events.vtools.ieee.org/m/268217 X-ALT-DESC:Description: <br /><p>Please join us for this year IEEE Montr&ea cute\;al Keynote Event:</p>\n<p>Claude Shannon's 1948 "A Mathematical Theo ry of Communication" provided the basis for the digital communication revo lution. As part of that ground-breaking work\, he identified the greatest rate (capacity) at which data can be communicated over a noisy channel. Hi s proposed algorithm used on random codes and a code centric maximum Maxim um Likelihood (ML) decoding\, where channel outputs are compared to all po ssible input codewords to select the most likely candidate based on the ob served channel output. Despite its mathematical elegance\, his code centri c decoding algorithm is impractical from a complexity perspective and much work in the intervening 70 years has focused on co-designing codes and de coders that enable reliable communication at high rates.</p>\n<div>\n<div> \n<div>\n<div>\n<div>\n<div>\n<p>In collaboration with Ken Duffy and his g roup\, we introduce a new algorithm\, Guessing Random Additive Noise Decea sing (GRAND) for a noise-centric\, rather than code-centric\, ML decoding. The receiver rank orders noise effect sequences from most likely to least likely\, and guesses accordingly. When inverting\, in decreasing order of likelihood\, noise effect sequences from the received signal\, the first instance that results in an element of the code-book is the ML decoding. O ur results show that\, with GRAND\, even extremely simple codes\, such as CRCs\, match or outperform state of the art code/decoder pairs\, indicatin g that the choice of decoder is likely to be more important than that of c ode.&nbsp\;</p>\n</div>\n<div>\n<p>&nbsp\;</p>\n</div>\n<div>\n<p>We illus trate the practical usefulness of our approach and discuss its hardware im plementation\, done with Rabia Yazicigil and her group. The complexity of the decoding is\, for the sorts of channels generally used in commercial a pplications\, quite low\, unlike code-centric ML and the chip is able to d ecode any linear code.&nbsp\;</p>\n</div>\n</div>\n</div>\n</div>\n</div>\ n</div> END:VEVENT END:VCALENDAR