SPC05-4: Successively Structured Gaussian CEO Problem
Bibliographic record
Abstract
We consider a distributed sensor network, modeled by the Chief Executive Officer (CEO) problem, in which sensors encode their observations without collaborating with each other and send through rate constrained noiseless channels to a fusion center (FC). We use the successive Wyner-Ziv coding strategy in this problem where sensors have differing quality of observations. We determine the optimal rate allocation scheme to obtain the minimum distortion under a sum-rate constraint. We show that the optimal sum-rate distortion performance for the Gaussian CEO problem is achievable using the successive coding strategy which is inherently a less complex way of obtaining a prescribed distortion. We also determine the achievable rate region and the optimal rate allocation region for the Gaussian CEO problem. We show that if the number of sensors tends to infinity while the sum-rate is finite, the performance of the successive coding strategy with equal rate sensors converges to the rate-distortion function. The same is true when the sum-rate tends to infinity with a finite number of sensors. Finally, we obtain the communication throughput of a K-relay network based on our results for the CEO problem.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".