Exploiting the <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-to-1 Mapping in Compress-and-Forward Relaying
Bibliographic record
Abstract
In this paper, a forward decoding procedure is developed for the compress-and-forward (CF) relaying scheme. This procedure uses a layered framework and is based on exploiting a feature of the N -to-1 mapping inherent in the underlying Wyner-Ziv binning. It is shown that exploiting this mapping enables the relaxation of the constraint on the rate of the relay codewords representing the bin indices. For the cooperative multimessage network, the proposed procedure achieves the same rate region as the short-message noisy network coding (SNNC) scheme. However, this procedure is more advantageous for other networks including the two networks presented herein. The first network is a relay chain one with two destinations, whereas the second network is a partially cooperative multimessage one with three destinations. In both networks, side information is available to a subset of the decoding nodes, but not to the rest of the nodes, and in both cases, the network benefits from the relaxation of the rate of the CF bin indices. This relaxation results in rate regions larger than those achieved by the conventional CF and SNNC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".