BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20200308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20201101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210121T012844Z UID:12A49F40-26BF-4D25-90E7-C8CF4F0D1F58 DTSTART;TZID=US/Eastern:20201028T181500 DTEND;TZID=US/Eastern:20201028T201500 DESCRIPTION:Guessing Random Additive Noise Decoding (GRAND)\n\nWe introduce a new algorithm for a noise-centric\, rather than code-centric\, ML decod ing. The algorithm is based on the principle that the receiver rank orders noise sequences from most likely to least likely\, and guesses noises acc ordingly.\n\nAbstract: Claude Shannon's 1948 "A Mathematical Theory of Com munication" provided the basis for the digital communication revolution. A s part of that ground-breaking work\, he identified the greatest rate (cap acity) at which data can be communicated over a noisy channel. He also pro vided an algorithm for achieving it\, based on random codes and a code-cen tric Maximum Likelihood (ML) decoding\, where channel outputs are compared to all possible codewords to select the most likely candidate based on th e observed output. Despite its mathematical elegance\, his algorithm is im practical from a complexity perspective and much work in the intervening 7 0 years has focused on co-designing codes and decoders that enable reliabl e communication at high rates.\n\nWe introduce a new algorithm for a noise -centric\, rather than code-centric\, ML decoding. The algorithm is based on the principle that the receiver rank orders noise sequences from most l ikely to least likely and guesses noises accordingly. Subtracting noise fr om the received signal in that order\, the first instance that results in an element of the code-book is the ML decoding. For common additive noise channels\, we establish that the algorithm is capacity-achieving for unifo rmly selected code-books\, providing an intuitive alternate approach to th e channel coding theorem. We illustrate the practical usefulness of our ap proach and the fact that it renders the decoding of random codes feasible. The complexity of the decoding is\, for the sorts of channels generally u sed in commercial applications\, quite low\, unlike code-centric ML\n\nCo- sponsored by: Chamara Johnson\, Chair\, VTS chapter NY section\n\nSpeaker( s): Dr. Muriel Medard\, \n\nAgenda: \n6.15pm to 6.20pm: Login and checks\n \n6.20pm: Introduce speaker\; 6.30pm - 8.00pm: Talk "Guessing Random Addit ive Noise Decoding (GRAND)"\n\n8.00pm - 8.15pm Q & A session 8.00pm Closin g remarks\n\nHolmdel\, New Jersey\, United States\, 07733\, Virtual: https ://events.vtools.ieee.org/m/241603 LOCATION:Holmdel\, New Jersey\, United States\, 07733\, Virtual: https://ev ents.vtools.ieee.org/m/241603 ORGANIZER:Raghunandan@ieee.org SEQUENCE:8 SUMMARY:CAPACITY OF WIRELESS NETWORKS - A NEW APPROACH URL;VALUE=URI:https://events.vtools.ieee.org/m/241603 X-ALT-DESC:Description: <br /><p><strong>Guessing Random Additive Noise Dec oding (GRAND)</strong></p>\n<p>We introduce a new algorithm for a noise-ce ntric\, rather than code-centric\, ML decoding. The algorithm is based on the principle that the receiver rank orders noise sequences from most like ly to least likely\, and guesses noises accordingly.</p>\n<p><strong><u>Ab stract:</u></strong> Claude Shannon's 1948 "A Mathematical Theory of Commu nication" provided the basis for the digital communication revolution. As part of that ground-breaking work\, he identified the greatest rate (capac ity) at which data can be communicated over a noisy channel. He also provi ded an algorithm for achieving it\, based on random codes and a code-centr ic Maximum Likelihood (ML) decoding\, where channel outputs are compared t o all possible codewords to select the most likely candidate based on the observed output. Despite its mathematical elegance\, his algorithm is impr actical from a complexity perspective and much work in the intervening 70 years has focused on co-designing codes and decoders that enable reliable communication at high rates.</p>\n<p>&nbsp\;</p>\n<p>We introduce a new al gorithm for a noise-centric\, rather than code-centric\, ML decoding. The algorithm is based on the principle that the receiver rank orders noise se quences from most likely to least likely and guesses noises accordingly. S ubtracting noise from the received signal in that order\, the first instan ce that results in an element of the code-book is the ML decoding. For com mon additive noise channels\, we establish that the algorithm is capacity- achieving for uniformly selected code-books\, providing an intuitive alter nate approach to the channel coding theorem. &nbsp\;We illustrate the prac tical usefulness of our approach and the fact that it renders the decoding of random codes feasible. The complexity of the decoding is\, for the sor ts of channels generally used in commercial applications\, quite low\, unl ike code-centric ML</p><br /><br />Agenda: <br /><p>6.15pm to 6.20pm: Logi n and checks</p>\n<p>6.20pm: Introduce speaker\; 6.30pm - 8.00pm:&nbsp\;Ta lk "Guessing Random Additive Noise Decoding (GRAND)"</p>\n<p>8.00pm - 8.15 pm&nbsp\; Q &amp\; A session 8.00pm Closing remarks</p> END:VEVENT END:VCALENDAR