Hybrid digital-analog joint source-channel coding for broadcasting correlated Gaussian sources
Bibliographic record
Abstract
We consider the transmission of a bivariate Gaussian source S = (S1, S2) across a power-limited two-user Gaussian broadcast channel. User i (i = 1, 2) observes the transmitted signal corrupted by Gaussian noise with power sigmai2and wants to estimate Si. We study hybrid digital-analog (HDA) joint source-channel coding schemes and analyze these schemes to obtain achievable (squared-error) distortion regions. Two cases are considered: 1) source and channel bandwidths are equal, 2) broadcasting with bandwidth compression. We adapt HDA schemes of Wilson et al. and Prabhakaran et al. to provide various achievable distortion regions for both cases. Using numerical examples, we demonstrate that for bandwidth compression, a three-layered coding scheme consisting of analog, superposition, and Costa coding performs well compared to the other provided HDA schemes. In the case of matched bandwidth, a three-layered coding scheme with an analog layer and two layers, each consisting of a Wyner-Ziv coder followed by a Costa coder, performs best.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".