On predictive coding for erasure channels using a Kalman framework

Thomas Arildsen, Manohar N. Murthi, Søren Vang Andersen, Søren Holdt Jensen

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

We present a new design method for robust low-delay coding of autoregressive (AR) sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation (DPCM) encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.

Original languageEnglish (US)
Pages (from-to)4456-4466
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume57
Issue number11
DOIs
StatePublished - Nov 4 2009

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Keywords

  • Differential pulse code modulation (DPCM)
  • Erasure channels
  • Joint source-channel coding
  • Kalman filtering
  • Linear predictive coding
  • Quantization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

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