On the design of robust vector quantizers for sources and channels with memory
Bibliographic record
Abstract
Recently, robust quantization has leceived conside¡able attention, particularly as a potential approach to joint source-channel coding.This dissertation investigates the practical design of channel optimized vector quantizers (COVQ).Specific problems considered here include COVQs with memory and COVQs operating over channels uith memorg.In this work, the emphasis is placed on soft-d,ecod,i,ng at the receiver.Vector quantizers with memoty ale an effective means of quantizing correlated signals.However, when designed without regard to channel errors, these quantizers sufier from degradation of performance due to the propagation of channel e¡rors at the receiver.We consider two important examples of such quantizers, namely, predi.cti.aeuector quantizers (PVQ) and f,nite-state uector quantizers (FSVQ).In the case of PVQ, an iterative algoriihm is developed for jointly optimizing the quantizer and the associated linear predictor to a given channel.According to the simulation results presented here, the proposed PVQ designs based on hard-decoding perform comparably to those obtained by a previously studied gradient-search optimization algorithm.F\rthermore, it is demonstrated that PVQs with soft-decoding can provide a significant implovement over hald-decoding systems.In the case of FSVQ, a time- recursive decoding algorithm, rvhich exhibits graceful degladation of perfor.mancewith increasing channel noise, is int¡oduced, Design of channel optimized FSVe is also considered.Simulation results are presented, which demonstrate that proposed channel optimized FSVQs outperforur the memoryless COVes operating at the same râte.ll Finally, in the context of channels with memor¡ joint equalization and soft- decoding using a sliding-block decoder is investigated.This decoder is a non-linea.rtime-invariant filter based on minimurn mean square error criterion.As a practi- cai implementation, multi-layet perceptron (MLP) is considered.Simulation results indicate that MLP-based soft-decoder outperforms a previously studied recursive soft- decoder, particularly under high channel noise.However, the complexity of the optimal sliding-block decode¡ function is found to increase with the encoder resolution, rnaking the estimation task halder.In an encouraging development, it is shown that the optimal sliding-block decoder for the Gaussian channel approximates a linear function as the channel becomes noisier.Experimental results seem to support this theoretical result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".