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Energy and sampling constrained asynchronous communication

  • MIT Lincoln Laboratory
  • University of Southern California

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

Abstract

The minimum energy, and, more generally, the minimum input cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver can sample at no cost all channel outputs. Suppose now there is a cost associated to output sampling and that the receiver is constrained to observe only a fraction ρ (0, 1] of all channel outputs. What is the input cost penalty due to sparse output sampling? Remarkably, there is no penalty: regardless of ρ > 0 the asynchronous capacity per unit cost is the same as under full sampling, i.e., when ρ = 1. Moreover, there is no penalty in terms of decoding delay with respect to full sampling. This latter result relies on the possibility to sample adaptively; the next sample is a function of past samples. When sampling is non-adaptive it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/ρ. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Information Theory, ISIT 2013
Pages2518-2522
Number of pages5
DOIs
Publication statusPublished - 19 Dec 2013
Event2013 IEEE International Symposium on Information Theory, ISIT 2013 - Istanbul, Turkey
Duration: 7 Jul 201312 Jul 2013

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Conference

Conference2013 IEEE International Symposium on Information Theory, ISIT 2013
Country/TerritoryTurkey
CityIstanbul
Period7/07/1312/07/13

Keywords

  • Asynchronous communication
  • bursty communication
  • capacity per unit cost
  • energy
  • sensor networks
  • sequential decoding
  • sparse communication
  • sparse sampling
  • synchronization

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