On the Compound Broadcast Channel: Multiple Description Coding and\n Interference Decoding
Bibliographic record
Abstract
This work investigates the general two-user Compound Broadcast Channel (BC)\nwhere an encoder wishes to transmit common and private messages to two\nreceivers while being oblivious to two possible channel realizations\ncontrolling the communication. The focus is on the characterization of the\nlargest achievable rate region by resorting to more evolved encoding and\ndecoding techniques than the conventional coding for the standard BC. The role\nof the decoder is first explored, and an achievable rate region is derived\nbased on the principle of "Interference Decoding" (ID) where each receiver\ndecodes its intended message and chooses to (non-uniquely) decode or not the\ninterfering message. This inner bound is shown to be capacity achieving for a\nclass of non-trivial compound BEC/BSC broadcast channels while the worst-case\nof Marton's inner bound -based on "Non Interference Decoding" (NID)- fails to\nachieve the capacity region. The role of the encoder is then studied, and an\nachievable rate region is derived based on "Multiple Description" (MD) coding\nwhere the encoder transmits a common as well as multiple dedicated private\ndescriptions to the many instances of the users channels. It turns out that MD\ncoding outperforms the single description scheme -Common Description (CD)\ncoding- for a class of compound Multiple Input Single Output Broadcast Channels\n(MISO BC).\n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".