Joint channel estimation and data transmission: Achievable rates

Satish Vedantam, Wenyi Zhang, Urbashi Mitra, Ashutosh Sabharwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


A formulation for a joint communication and estimation problem is proposed: simultaneous communication over a noisy channel and estimation of certain channel parameters are desired. We are interested in quantifying the tradeoff between the achievable rate and distortion in estimating the channel parameters. Two particular sample channels are considered and achievable rates for these channels are determined. First, the binary symmetric channel is examined; an achievable capacitydistortion tradeoff is derived for both joint and time-orthogonal protocols. For the flat fading, additive, white, Gaussian noise channel, a novel joint communication and estimation scheme using low correlation sequences is presented. It is observed that in most situations, joint communication and estimation performs better than a scheme where communication and estimation are performed individually, furthermore, the gains of joint communication and estimation over individual communication and estimation can be significant as the distortion tolerance increases. Finally, it is observed that even a slight tolerance to errors in the channel parameters close to the theoretical lower bounds yield significant improvements in the rate at which reliable communication is achievable.

Original languageEnglish (US)
Title of host publication2007 IEEE Information Theory Workshop, ITW 2007, Proceedings
Number of pages6
StatePublished - 2007
Event2007 IEEE Information Theory Workshop, ITW 2007 - Lake Tahoe, CA, United States
Duration: Sep 2 2007Sep 6 2007

Publication series

Name2007 IEEE Information Theory Workshop, ITW 2007, Proceedings


Conference2007 IEEE Information Theory Workshop, ITW 2007
Country/TerritoryUnited States
CityLake Tahoe, CA

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Information Systems and Management


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