Sequence MMSE Source Decoding Over Noisy Channels Using the Residual Redundancies
Bibliographic record
Abstract
In this work, we consider the problem of decoding a predictively encoded signal over a noisy channel when there is a residual redundancy (captured by a γ-order Markov model) in the sequence of transmitted data. Our objective is to minimize the mean squared error in the reconstruction of the original signal (input to the predictive source coder). The problem is formulated and solved through Minimum Mean Squared Error (MMSE) decoding of a sequence of samples over a memoryless noisy channel, which was previously recognized to be an open problem by Phamdo and Farvardin in [3]. The related previous works include a sequence MAP decoder [2] and several VQ MMSE decoders which all use a first-order Markov model for the residual redundancy. The former is suboptimal when the performance criterion is the mean squared error and the latter schemes are suboptimal since they decode the data samples received over the channel (the prediction residues) rather than the original signal. As well, using a first-order model, they fail to utilize all the remaining redundancy in the decoding process. The solution is setup by modeling the source and its redundancy with a trellis structure.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".