Joint source-channel turbo coding for binary Markov sources
IEEE Transactions on Wireless Communications
Context weighting for general finite-context sources
IEEE Transactions on Information Theory
The empirical distribution of good codes
IEEE Transactions on Information Theory
Systematic lossy source/channel coding
IEEE Transactions on Information Theory
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Low-complexity sequential lossless coding for piecewise-stationary memoryless sources
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Universal lossless source coding with the Burrows Wheeler transform
IEEE Transactions on Information Theory
Linear time universal coding and time reversal of tree sources via FSM closure
IEEE Transactions on Information Theory
Universal discrete denoising: known channel
IEEE Transactions on Information Theory
A universal finite memory source
IEEE Transactions on Information Theory
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
Joint turbo decoding and estimation of hidden Markov sources
IEEE Journal on Selected Areas in Communications
Wireless Personal Communications: An International Journal
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Utilization of redundancy left in a channel coded sequence can improve channel decoding performance. Stronger improvement can usually be achieved with nonsystematic encoding. However, nonsystematic codes recently proposed for this problem are not robust to the statistical parameters governing a sequence and thus should not be used without prior knowledge of these parameters. In this work, decoders of nonsystematic quick-look-in turbo codes are adapted to extract and exploit redundancy left in coded data to improve channel decoding performance. Methods, based on universal compression and denoising, for extracting the governing statistical parameters for various source models are integrated into the channel decoder by also taking advantage of the code structure. Simulation results demonstrate significant performance gains over standard systematic codes that can be achieved with the new methods for a wide range of statistical models and governing parameters. In many cases, performance almost as good as that with perfect knowledge of the governing parameters is achievable.