Analog coding for Gaussian source and state interference estimation
Bibliographic record
Abstract
We consider zero-delay analog coding of a Gaussian source over a Gaussian channel with additive correlated Gaussian interference known to the transmitter. The receiver aims to jointly estimate the source signal and the state interference. We propose a layered parametric analog coding scheme based on linear and sawtooth mappings. We derive an upper bound on the distortion for the parametric scheme by assuming a suboptimal decoder. To optimize the system parameters, we use two suboptimal methods. The first one is partially numerical and part of the parameters are derived assuming no sawtooth mapping; the other one, however, is based on minimizing the derived upper bound. To improve the performance whenever storage and offline design complexity are not an issue, we design a nonparametric mapping through an iterative process based on joint optimization between the encoder and the decoder using the necessary conditions for optimality. Numerical results show that the nonparametric and parametric mappings outperform the linear scheme and overcome the saturation effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".