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Dual Domain Expurgated Error Exponents for Source Coding with Side Information

Mehdi Dabirnia, Hamdi Joudeh, Albert Guillén i Fàbregas

TL;DR

An expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents and shows that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.

Abstract

We introduce an expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents. Dual-domain methods yield optimization problems over few parameters, with any sub-optimal choice resulting in an achievable exponent, as opposed to primal-domain optimization over distributions. In addition, dual-domain methods naturally allow for general alphabets and/or memory. We derive two such expurgated error exponents for different random-coding ensembles in the case where the decoder is possibly mismatched with respect to the source and side information joint distribution. We show the better of the exponents coincides with the Csiszár-Körner exponent obtained via a graph decomposition lemma. We show some numerical examples that illustrate the differences between the two exponents and show that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.

Dual Domain Expurgated Error Exponents for Source Coding with Side Information

TL;DR

An expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents and shows that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.

Abstract

We introduce an expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents. Dual-domain methods yield optimization problems over few parameters, with any sub-optimal choice resulting in an achievable exponent, as opposed to primal-domain optimization over distributions. In addition, dual-domain methods naturally allow for general alphabets and/or memory. We derive two such expurgated error exponents for different random-coding ensembles in the case where the decoder is possibly mismatched with respect to the source and side information joint distribution. We show the better of the exponents coincides with the Csiszár-Körner exponent obtained via a graph decomposition lemma. We show some numerical examples that illustrate the differences between the two exponents and show that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.

Paper Structure

This paper contains 18 sections, 9 theorems, 126 equations, 1 figure.

Key Result

Theorem 1

For every $R>0$ and every distribution $P_{XY}\in\mathcal{P}(\mathcal{X}\times\mathcal{Y})$ there exists a standard block source code with maximum metric decoder eq:dec employing decoding metric $q(x,y)$ that achieves the exponent where and and the optimization is over real parameters $\rho$ and $s$.

Figures (1)

  • Figure 1: Error exponents for the source $X$ and side information $Y$ with joint distribution given in \ref{['eqn:example_joint_distribution']}. The mismatched decoder uses the minimum Hamming distance metric.

Theorems & Definitions (18)

  • Definition 1
  • Theorem 1
  • proof
  • Definition 2
  • Theorem 2: Type-by-Type Random Coding
  • proof
  • Proposition 1: Primal-dual equivalence
  • proof
  • Corollary 1
  • proof
  • ...and 8 more