In this paper, finite-state vector quantization (FSVQ) over noisy channels is studied. In particular a robust, time-recursive algorithm is proposed for reconstructing the output from a finite-state encoder, observed t...
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ISBN:
(纸本)0780374029
In this paper, finite-state vector quantization (FSVQ) over noisy channels is studied. In particular a robust, time-recursive algorithm is proposed for reconstructing the output from a finite-state encoder, observed through a noisy channel. In contrast to an ordinary finite-state decoder, the proposed decoder exhibits graceful degradation of performance with increasing channel noise. We also consider the iterative optimization of encoder and decoder for designing channel optimized FSVQ. Simulation results based on Gauss-Markov source and additive white Gaussian noise channel are presented, and it is shown that robust FSVQ designed by methodology introduced here can outperform memoryless channel optimized vector quantization at the same rate. Soft-decoding at the receiver, which provides an additional performance gain, is also considered.
Time-varying encoders for constrained systems are introduced. The approach generalizes the state-splitting (ACH) algorithm in a way that yields encoders consisting of multiple phases, with encoding proceeding cyclical...
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Time-varying encoders for constrained systems are introduced. The approach generalizes the state-splitting (ACH) algorithm in a way that yields encoders consisting of multiple phases, with encoding proceeding cyclically from one phase to the next, The framework is useful for design of high-rate codes with reduced decoder error propagation and reduced complexity.
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