End-to-end distortion analysis of multicasting over orthogonal receive component decode-forward cooperative broadcast channels
Bibliographic record
Abstract
This paper studies the end to end distortion of transmission of a layered encoded source over a cooperative relay broadcast channel. A jointly optimised source-channel code is proposed to multicast a two-layered encoded source to two destinations. A degraded discrete memoryless broadcast channel is assumed, where the first destination with better channel conditions decodes both layers consisting of the common base layer and the refining layer for source reconstruction at a higher quality. Meanwhile, the second destination only decodes the base layer for a lower quality reconstruction of the source. The first destination cooperates in transmitting the common base layer to the second destination using a decode-forward (DF) relaying protocol over an orthogonal receive component (ORC) relay channel. An inner bound on the capacity region of the ORC-DF cooperative broadcast channel with a degraded message set is derived and the end to end distortion of reconstructing the source at both of the destinations is characterized. The achievable rate region and distortion performance of such network are demonstrated to outperform another variant of a DF-based cooperative broadcast channel, as well as with the non-cooperative broadcast channel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".