An Information Theoretic Charachterization of Channel Shortening Receivers

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


Optimal data detection of a linear channel can always be implemented through the Viterbi algorithm (VA). However, in many cases of interest the memory of the channel prohibits application of the VA. A popular and conceptually simple method in this case, studied since the early 70s, is to first filter the received signal in order to shorten the memory of the channel, and then to apply a VA that operates with the shorter memory. We shall refer to this as a channel shortening (CS) receiver. Although studied for almost four decades, an information theoretic understanding of what such simple receiver solution is actually doing is not available. In this paper we will show that an optimized CS receiver is implementing the chain rule of mutual information, but only up to the shortened memory that the receiver is operating with. Further, we will show that the tools for analyzing the ensuing achievable rates from an optimized CS receiver are precisely the same as the tools that are used for analyzing the achievable rates of an minimum mean square error (MMSE) receiver.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Electrical Engineering, Electronic Engineering, Information Engineering


  • Receiver design, channel shortening detection, reduced complexity, detection, mismatched receivers, mismatched mutual information
Original languageEnglish
Title of host publication2013 Asilomar Conference on Signals, Systems and Computers
PublisherIEEE--Institute of Electrical and Electronics Engineers Inc.
Publication statusPublished - 2013
Publication categoryResearch
Event47th Annual Asilomar Conference on Signals, Systems, and Computers, 2003 - Pacific Grove, CA, Pacific Grove, CA, United States
Duration: 2003 Nov 32003 Nov 6
Conference number: 47

Publication series

ISSN (Print)1058-6393


Conference47th Annual Asilomar Conference on Signals, Systems, and Computers, 2003
CountryUnited States
CityPacific Grove, CA

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