The objective of this work is to investigate how channel optimization techniques ran be applied to predictive vector quantizers. In particular, an efficient encoder search procedure and two design methods are derived....
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The objective of this work is to investigate how channel optimization techniques ran be applied to predictive vector quantizers. In particular, an efficient encoder search procedure and two design methods are derived. The design methods proposed here, one sample iterative and one block iterative, simultaneously optimize the predictor and the codebook, Extensive simulations show the advantage of this quantization method compared to other memory based quantization schemes as well as memoryless VQ, We also demonstrate that the design methods can be used to obtain index assignments that are advantageous to what is obtained by post process index assignment algorithms.
We design a channeloptimizedvector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing...
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We design a channeloptimizedvector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing the quantized source via the MAP decoder and iteratively optimize the overall discrete channel (at the symbol level) jointly with the quantizer. We consider memoryless Gaussian and Gauss-Markov sources transmitted over a binary phase-shift keying modulated Rayleigh fading channel. Our scheme has less encoding computational and storage complexity (particularly for noisy channel conditions) than both conventional and soft-decision COVQ systems, which use hard-decision and soft-decision maximum likelihood demodulation, respectively. Furthermore, it provides a notable signal-to-distortion ratio gain over the former system, and in some cases it matches or outperforms the latter one.
Data transmission using self-organizing feature map (SOFM) based vector quantizer (VQ) and channeloptimizedvector quantizer (COVQ) with various space-time trellis codes (ST`TC) over wireless channels are studied. It...
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ISBN:
(纸本)0780381858
Data transmission using self-organizing feature map (SOFM) based vector quantizer (VQ) and channeloptimizedvector quantizer (COVQ) with various space-time trellis codes (ST`TC) over wireless channels are studied. It is interesting to note that in a SOFM-STTC system, even with a weak STTC, the overall performance in terms of reconstruction error does not degrade a lot, compared with a stronger STTC. Hence, a system of low complexity but satisfactory performance could be built. Our simulation results also show that the performance of SOFM-STTC is marginally worse than that of COVQ.
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