On Predictive Coding for Erasure Channels Using a Kalman Framework

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 origina...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 57; H. 11; S. 4456 - 4466
Hauptverfasser:	Arildsen, T., Murthi, M.N., Andersen, S.V., Jensen, S.H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.11.2009 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Applied sciences Channels Coders Coding Coding, codes Decoders Design engineering Design methodology Detection, estimation, filtering, equalization, prediction Differential pulse code modulation (DPCM) Encoders erasure channels Errors Exact sciences and technology Information, signal and communications theory Iterative decoding joint source-channel coding Kalman filtering Kalman filters Linear predictive coding Mathematical models Modulation, demodulation Predictive coding Propagation losses Pulse modulation Quantization Robustness Sampling, quantization Signal and communications theory Signal, noise Studies Telecommunications and information theory Performance evaluation Iterative method Autoregressive model Joint source channel coding Linear model Codec Covariance Signal quantization Off line Kalman filtering Delay time quantization joint source-channel coding Transmission channel Linear predictive coding Decoding Algorithm Pulse code modulation Quantization noise Differential pulse code modulation (DPCM) Predictive coding Signal processing erasure channels Differential modulation Signal to noise ratio
ISSN:	1053-587X, 1941-0476
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2009.2025796