Distortion bounds for broadcasting a Gaussian source in the presence of interference
Bibliographic record
Abstract
We consider the transmission of a Gaussian source over the two-user Gaussian broadcast channel in the presence of interference that is known to the transmitter. The interference is assumed to be correlated to the source and each user is interested in estimating the source signal. We propose a hybrid digital-analog (HDA) scheme based on proper combinations of power splitting, Wyner-Ziv and HDA Costa coding. The achievable (square-error) distortion region (inner bound) of this scheme is analyzed under source-channel bandwidth expansion; the matched bandwidth is treated as a special case. An outer bound on the distortion region is also derived by assuming full/partial knowledge of the interference at both users and by adapting the approach of Reznic et al. (2006). This outer bound can be tighter than the “trivial” outer bound found by assuming point-to-point communication.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".