Robust predictive quantization: A new analysis and optimization framework

Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal, Kannan Ramchandran

Research output: Contribution to journalConference articlepeer-review


This work is focused on computing-via a deterministic optimization with linear matrix inequality (LMI) constraints, rather than a pseudorandom simulation-the performance of predictive quantization schemes under various scenarios for loss and degradation of encoded prediction error samples. The ability to make this computation then allows for the optimization of prediction filters with the aim of minimizing overall mean squared error (including the effects of losses) rather than to minimize the variance of the unquantized prediction error sequence. The main tools are recent characterizations of asymptotic state estimation error covariance and output estimation error variance in terms of LMIs. These characterizations apply to discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. Translating to the signal processing terminology, this means that the signal model is "piecewise ARMA," as is standard in many forms of speech processing.

Original languageEnglish (US)
Number of pages1
JournalIEEE International Symposium on Information Theory - Proceedings
StatePublished - 2004
EventProceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States
Duration: Jun 27 2004Jul 2 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


Dive into the research topics of 'Robust predictive quantization: A new analysis and optimization framework'. Together they form a unique fingerprint.

Cite this