On the linear codebook-level duality between Slepian-Wolf coding and channel coding

  • Authors:
  • Jun Chen;Da-Ke He;Ashish Jagmohan;Luis A. Lastras-Montaño;En-Hui Yang

  • Affiliations:
  • Department of Electrical and Computer Engineering, Mc-Master University, Hamilton, ON, Canada;SlipStream Data, Research In Motion, Waterloo, ON, Canada and IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2009

Quantified Score

Hi-index 754.84

Visualization

Abstract

In this paper, it is shown that each Slepian-Wolf coding problem is related to a dual channel coding problem in the sense that the sphere packing exponents, random coding exponents, and correct decoding exponents in these two problems are mirror-symmetrical to each other. This mirror symmetry is interpreted as a manifestation of the linear codebook-level duality between Slepian-Wolf coding and channel coding. Furthermore, this duality, in conjunction with a systematic analysis of the expurgated exponents, reveals that nonlinear Slepian-Wolf codes can strictly outperform linear Slepian-Wolf codes in terms of rate-error tradeoff at high rates. The linear codebook-level duality is also established for general sources and channels.