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Abstract
Source coding concerns the representation of information in a source signal using as few bits as possible. In the case of lossy source coding, it is the encoding of a source signal using the fewest possible bits at a given distortion or, at the lowest possible distortion given a specified bit rate. Channel coding is usually applied in combination with source coding to ensure reliable transmission of the (source coded) information at the maximal rate across a channel given the properties of this channel.
In this thesis, we consider the coding of autoregressive (AR) sources which are sources that can be modeled as autoregressive processes. The
coding of AR sources lends itself to linear predictive coding. We address the problem of joint source/channel coding in the setting of linear predictive coding of AR sources. We consider channels in which individual source coded signal samples can be lost during channel transmission. The optimization of linear predictive coding for such lossy channel behaviour is not well understood in the literature.
We review basics of source and channel coding, differential pulse code modulation (DPCM), statespace models, minimum mean squared error (MMSE) estimation, and quantization. On this background we propose a new algorithm for optimization of predictive coding of AR sources for transmission across channels with loss.
The optimization algorithm takes as its starting point a rethinking of the source coding operation as an operation producing linear measurements of the source signal. The source process and source encoder are formulated as a statespace model, enabling the use of Kalman filtering for decoding the source signal.
The optimization algorithm is a greedy approach that designs the filter coefficients of a generalized DPCM encoder. The objective of the optimization problem (design of the filter coefficients) is to minimize the decoder state error covariance. This is done iteratively in a greedy sense, minimizing the trace of the state error covariance at each iteration until convergence.
Furthermore, it is proved that employing fixedlag smoothing at the decoder is guaranteed to reduce the estimated source signal mean squared error (MSE) under mild constraints on the encoder filter coefficients.
Extensive Monte Carlo simulation studies show that the proposed algorithm improves the signaltonoise ratio (SNR) of decoded source signals substantially compared to the case where the encoder is unaware of channel loss.
We finally provide an extensive overview of crosslayer communication issues which are important to consider due to the fact that the proposed algorithm interacts with the source coding and exploits channelrelated information typically available from different layers of network protocol stacks.
In this thesis, we consider the coding of autoregressive (AR) sources which are sources that can be modeled as autoregressive processes. The
coding of AR sources lends itself to linear predictive coding. We address the problem of joint source/channel coding in the setting of linear predictive coding of AR sources. We consider channels in which individual source coded signal samples can be lost during channel transmission. The optimization of linear predictive coding for such lossy channel behaviour is not well understood in the literature.
We review basics of source and channel coding, differential pulse code modulation (DPCM), statespace models, minimum mean squared error (MMSE) estimation, and quantization. On this background we propose a new algorithm for optimization of predictive coding of AR sources for transmission across channels with loss.
The optimization algorithm takes as its starting point a rethinking of the source coding operation as an operation producing linear measurements of the source signal. The source process and source encoder are formulated as a statespace model, enabling the use of Kalman filtering for decoding the source signal.
The optimization algorithm is a greedy approach that designs the filter coefficients of a generalized DPCM encoder. The objective of the optimization problem (design of the filter coefficients) is to minimize the decoder state error covariance. This is done iteratively in a greedy sense, minimizing the trace of the state error covariance at each iteration until convergence.
Furthermore, it is proved that employing fixedlag smoothing at the decoder is guaranteed to reduce the estimated source signal mean squared error (MSE) under mild constraints on the encoder filter coefficients.
Extensive Monte Carlo simulation studies show that the proposed algorithm improves the signaltonoise ratio (SNR) of decoded source signals substantially compared to the case where the encoder is unaware of channel loss.
We finally provide an extensive overview of crosslayer communication issues which are important to consider due to the fact that the proposed algorithm interacts with the source coding and exploits channelrelated information typically available from different layers of network protocol stacks.
Translated title of the contribution  Optimering af kodning af autoregressive signaler til transmission gennem kanaler med tab 

Original language  English 
Place of Publication  Aalborg 
Publisher  
Print ISBNs  9788792328526 
DOIs  
Publication status  Published  22 Dec 2010 
Keywords
 differential pulse code modulation
 Kalman filtering
 erasure channels
 joint sourcechannel coding
 linear predictive coding
 quantization
 fixedlag smooting
Activities
 1 Visiting another research institution

Electrical and Computer Engineering, University of Miami
Thomas Arildsen (Visiting researcher)
6 Feb 2007 → 4 Apr 2007Activity: Visiting another research institution