Sending Correlated Gaussian Sources over a Gaussian MAC: To Code, or not to Code
Bibliographic record
Abstract
We consider 1-helper problem in which one source provides partial side information to the fusion center (FC) to help reconstruction of the main source signal. Both sources communicate information about their observations to the FC through an additive white Gaussian multiple access channel (MAC) without cooperating with each other. Two types of MAC are considered: orthogonal MAC and interfering (non-orthogonal) MAC. We characterize the tradeoff between the transmission cost, i.e., power, and the estimation distortion, D, using Shannon's separation source and channel coding theorem. We demonstrate that the separation-based coding strategy outperforms the uncoded transmission under an orthogonal MAC. However, in the symmetric case under an interfering MAC, below a certain signal- to-noise ratio (SNR) threshold, uncoded transmission outperforms the separation-based scheme. The threshold can be determined in terms of the correlation coefficient between the sources, p, and in fact is an increasing function of p. Finally, the optimal power scheduling to minimize the total power consumption in the network is derived.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".