On the Optimal Power-Distortion Region for Asymmetric Gaussian Sensor Networks with Fading
Bibliographic record
Abstract
We consider the estimation of a Gaussian source by a Gaussian sensor network where L distributed sensors transmit noisy observations of the source through a fading Gaussian multiple access channel (MAC) to a fusion center (FC). Since sensor power is usually limited, our goal is to characterize the optimal tradeoff between the transmission cost, i.e., the power vector P = (P1, P2, ..., PL), and the average estimation distortion, D. We focus on asymmetric fading sensor networks in which the sensors have differing signal to noise ratios and transmission powers. We present necessary and sufficient conditions for the achievability of (L + 1)-tuples (P1, P2, ..., PL, D). For a symmetric Gaussian sensor network with deterministic and equal-magnitude fading, we derive the optimal power-distortion tradeoff. We also provide an achievable power-distortion region for the asymmetric sensor network with deterministic fading by analyzing the transmission of scaled versions of vector-quantized observations. We show that some of the power-distortion tuples achievable by this scheme are not achievable via an uncoded system.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".