Amplitude Constrained Poisson Noise Channel: Properties of the Capacity-Achieving Input Distribution

Alex Dytso, Luca Barletta, Shlomo Shamai Shitz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work considers a Poisson noise channel with an amplitude constraint. It is well-known that the capacity-achieving input distribution for this channel is discrete with finitely many points. We sharpen this result by introducing upper and lower bounds on the number of mass points. In particular, the upper bound of order A log2(A) and lower bound of order √A are established where A is the constraint on the input amplitude. In addition, along the way, we show several other properties of the capacity and capacity-achieving distribution. For example, it is shown that the capacity is equal to - log P_Y^*(0) where P_Y∗ is the optimal output distribution. Moreover, an upper bound on the values of the probability masses of the capacity-achieving distribution and a lower bound on the probability of the largest mass point are established.

Original languageEnglish
Title of host publication2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings
ISBN (Electronic)9781665403122
DOIs
StatePublished - 2021
Event2021 IEEE Information Theory Workshop, ITW 2021 - Virtual, Online, Japan
Duration: 17 Oct 202121 Oct 2021

Publication series

Name2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings

Conference

Conference2021 IEEE Information Theory Workshop, ITW 2021
Country/TerritoryJapan
CityVirtual, Online
Period17/10/2121/10/21

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software

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