Joint optimization of power scheduling and rate‐distortion performance in<i>one‐helper</i>problem
Bibliographic record
Abstract
Abstract We consider themany‐help‐oneproblem, also calledm‐helperproblem, for the special case ofm = 1 where one source provides partial side information to the fusion center (FC) to help reconstruction of the other correlated source. Both correlated sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. First, we characterize the optimal tradeoff between the transmission cost, that is, power, and the distortion D. Then, we consider a joint optimization of source coding and power scheduling from an information theory perspective, where the power scheduling is verified using Shannon capacity formula and the source‐coding problem is analyzed using rate‐distortion theory. We show that the joint optimization in the Gaussianone‐helperproblem can be solved analytically. We provide closed‐form expressions for the optimal distortion and the optimal power scheduling in terms of the cost weights. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".