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DESCRIPTION:Claude Shannon's 1948 "A Mathematical Theory of Communication" 
 provided the basis for the digital communication revolution. As part of th
 at ground-breaking work\, he identified the greatest rate (capacity) at wh
 ich data can be communicated over a noisy channel. He also provided an alg
 orithm for achieving it\, based on random codes and a code-centric Maximum
  Likelihood (ML) decoding\, where channel outputs are compared to all poss
 ible codewords to select the most likely candidate based on the observed o
 utput. Despite its mathematical elegance\, his algorithm is impractical fr
 om a complexity perspective and much work in the intervening 70 years has 
 focused on co-designing codes and decoders that enable reliable communicat
 ion at high rates.\n\nIn collaboration with Ken Duffy and his group\, we i
 ntroduce a new algorithm\, Guessing Random Additive Noise Deceasing (GRAND
 ) for a noise-centric\, rather than code-centric\, ML decoding. The algori
 thm is based on the principle that the receiver rank orders noise sequence
 s from most likely to least likely\, and guesses noises accordingly. Subtr
 acting noise from the received signal in that order\, the first instance t
 hat results in an element of the code-book is the ML decoding. We illustra
 te the practical usefulness of our approach and discuss its hardware imple
 mentation\, done with Rabia Yazicigil and her group. The complexity of the
  decoding is\, for the sorts of channels generally used in commercial appl
 ications\, quite low\, unlike code-centric ML. Our results show that\, wit
 h GRAND\, even extremely simple codes match or outperform state of the art
  code/decoder pairs\, indicating that the choice of decoder is likely to b
 e more important than that of code.\n\nSpeaker(s): Sc. D. Muriel Médard\,
  \n\nGuadalajara\, Jalisco\, Mexico\, Virtual: https://events.vtools.ieee.
 org/m/265251
LOCATION:Guadalajara\, Jalisco\, Mexico\, Virtual: https://events.vtools.ie
 ee.org/m/265251
ORGANIZER:r.calderonr@ieee.org
SEQUENCE:10
SUMMARY:It's not the code\, it's the decoder. By Dr. Muriel Médard
URL;VALUE=URI:https://events.vtools.ieee.org/m/265251
X-ALT-DESC:Description: <br /><p class="p1">Claude Shannon's 1948 "A Mathem
 atical Theory of Communication" provided the basis for the digital communi
 cation revolution. As part of that ground-breaking work\, he identified th
 e greatest rate (capacity) at which data can be communicated over a noisy 
 channel. He also provided an algorithm for achieving it\, based on random 
 codes and a code-centric Maximum Likelihood (ML) decoding\, where channel 
 outputs are compared to all possible codewords to select the most likely c
 andidate based on the observed output. Despite its mathematical elegance\,
  his algorithm is impractical 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 class="p1">In c
 ollaboration with Ken Duffy and his group\, we introduce a new algorithm\,
  Guessing Random Additive Noise Deceasing (GRAND) for a noise-centric\, ra
 ther than code-centric\, ML decoding. The algorithm is based on the princi
 ple that the receiver rank orders noise sequences from most likely to leas
 t likely\, and guesses noises accordingly. Subtracting noise from the rece
 ived signal in that order\, the first instance that results in an element 
 of the code-book is the ML decoding. We illustrate the practical usefulnes
 s of our approach and discuss its hardware implementation\, done with Rabi
 a Yazicigil and her group. The complexity of the decoding is\, for the sor
 ts of channels generally used in commercial applications\, quite low\, unl
 ike code-centric ML. Our results show that\, with GRAND\, even extremely s
 imple codes match or outperform state of the art code/decoder pairs\, indi
 cating that the choice of decoder is likely to be more important than that
  of code.&nbsp\;</p>
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