Equalization through large-deviation bounds

S. Venkatesh, Petros G Voulgaris

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


Channel equalization methods are used to mitigate the effects of inter-symbol interference (ISI). Traditional methods, maximize the signal to noise ratio (SNR), as a means to convert an ISI channel into a memoryless AWGN channel. Nevertheless, SNR maximization is not reflective of the error probability and lead typically to suboptimal solutions. Our viewpoint is to directly characterize the overall probability of symbol error by means of a Chernoff type bound for a given channel/receiver combination. The main idea behind our technique is to exploit the randomness of transmitted symbols to average out ISI rather than invert the channel dynamics. The problem reduces to choosing a receiver that minimizes the exponent in the Chernoff bound. This problem is shown to reduce to a mixed convex optimization problem. We comment on how the solution methodology can have implications for a fundamental understanding of the tradeoff between channel uncertainty and bit error probability, a situation commonly encountered in wireless communications.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)0780379977
StatePublished - Jan 1 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis


  • AWGN
  • Additive white noise
  • Error probability
  • Gaussian noise
  • Interference
  • Laboratories
  • Noise cancellation
  • Random variables
  • Signal to noise ratio
  • Uncertainty

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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