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SUMMARY:The Capacity-Achieving Distribution for the Amplitude Constrained 
 Additive Gaussian Channel:  An Upper Bound on the Number of Mass Points.
DTSTART:20200313T110000
DTEND:20200313T120000
DTSTAMP:20260408T085111Z
UID:bf43dd0e8fd76b8a5b4599294e91ba692676abab78036b372675151a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Semih Yagli\nWe study the real\, complex\, and vector additive
  Gaussian channels with input amplitude constraints. For the real additive
  Gaussian channel model with a peak power constraint on the input\, a clas
 sical result by Smith implies that the capacity-achieving input distributi
 on is discrete with finitely many mass points. Similarly\, for the complex
  and vector additive Gaussian channels with input amplitude constraints\, 
 one can show that the multi-dimensional capacity achieving distributions i
 n these respective settings will have shelled structures with finitely man
 y shells. However\, an unfortunate deficiency in these surprising and usef
 ul results is that Smith’s method (and its sibling methods in other simi
 lar problems) uses the “proof by contradiction” technique. While it is
  already too hard to find the capacity-achieving distributions in these pr
 oblems\, because of this contradiction-based proof technique\, not even a 
 bound on their support sizes was previously available. In this work\, we p
 rovide an alternative and far simpler proof for the discrete nature of the
  capacity-achieving input distributions in these problems\, and being a co
 nstructive technique as it is\, this new method we show is able to produce
  an implicit upper bound on the number of mass points (or ‘number of she
 lls’  in the complex and vector cases). This paves an alternative way i
 n approaching many such problems in the literature.\n\nBiography:\nSemih r
 eceived his Bachelor of Science degree in Electrical and Electronics Engin
 eering in 2013\, his Bachelor of Science degree in Mathematics in 2014 bot
 h from Middle East Technical University\, and his Master of Arts degree in
  Electrical Engineering in 2016 from Princeton University. Currently\, he 
 is pursuing his Ph.D. degree in Electrical Engineering in Princeton Univer
 sity under the supervision of H. Vincent Poor. His research interest revol
 ve around information theory\, statistical modeling\, optimization\, theor
 y of detection\, estimation\, and most recently\, federated learning algor
 ithms.  
LOCATION:INR 113 https://plan.epfl.ch/?room==INR%20113
STATUS:CONFIRMED
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