Partial decode-forward coding schemes for the Gaussian two-way relay channel
Bibliographic record
Abstract
We design novel partial decode-forward (PDF) schemes for the Gaussian two-way relay channel with direct link. Different from pure decode-forward, each user divides its message into two parts and the relay decodes only one part of each. The relay then generates its codeword as a function of the two decoded parts and forwards to the two users. We propose PDF schemes for both the full- and half-duplex modes. In full duplex, the scheme is based on block Markov encoding and forward joint decoding over 2 consecutive blocks. In half duplex, the transmission is divided into 4 phases, in which one user transmits during the first phase, the other during the second phase, both users transmit during the third phase and the relay transmits during the last phase. The relay decodes a part of the messages from both users at the end of phase 3 and each user decodes only at the end of phase 4. Analysis and simulation show that if for one user, the direct link is stronger than the user-to-relay link, while for the other, the direct link is weaker, then PDF can achieve a rate region strictly larger than the time-shared region of pure decode-forward and direct transmission for both full- and half-duplex modes.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".