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Lattices for Distributed Source Coding: Jointly Gaussian Sources and Reconstruction of a Linear Function

  • Dinesh Krithivasan
  • S. Sandeep Pradhan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4851)

Abstract

Consider a pair of correlated Gaussian sources (X 1,X 2). Two separate encoders observe the two components and communicate compressed versions of their observations to a common decoder. The decoder is interested in reconstructing a linear combination of X 1 and X 2 to within a mean-square distortion of D. We obtain an inner bound to the optimal rate-distortion region for this problem. A portion of this inner bound is achieved by a scheme that reconstructs the linear function directly rather than reconstructing the individual components X 1 and X 2 first. This results in a better rate region for certain parameter values. Our coding scheme relies on lattice coding techniques in contrast to more prevalent random coding arguments used to demonstrate achievable rate regions in information theory. We then consider the case of linear reconstruction of K sources and provide an inner bound to the optimal rate-distortion region. Some parts of the inner bound are achieved using the following coding structure: lattice vector quantization followed by “correlated” lattice-structured binning.

Keywords

Random Vector Rate Region Linear Code Gaussian Source Lattice Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dinesh Krithivasan
    • 1
  • S. Sandeep Pradhan
    • 1
  1. 1.Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109USA

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