On the capacity of Pairwise Collaborative Networks
Bibliographic record
Abstract
We derive expressions for the achievable rate region of a collaborative coding scheme in a two-transmitter, two-receiver pairwise collaborative network (PCN) where one transmitter and receiver pair, namely relay pair, assists the other pair, namely the source pair, by partially decoding and forwarding the transmitted message to the intended receiver. The relay pair provides such assistance while handling a private message. We assume that users can use the past channel outputs and can transmit and receive at the same time and in the same frequency band. In this collaborative scheme, the transmitter of the source pair splits its information into two independent parts. Ironically, the relay pair employs the decode and forward coding to assist the source pair in delivering a part of its message and re-encodes the decoded message along with private message, which is intended to the receiver of the relay pair, and broadcasts the results. The receiver of the relay pair decodes both messages, retrieves the private message, re-encodes and transmits the decoded massage to the intended destination. We also characterize the achievable rate region for Gaussian PCN. Finally, we provide numerical results to study the rate trade off for the involved pairs. Numerical result shows that the collaboration offers gain when the channel gain between the users of the relay pair are strong. It also shows that if the channel conditions between transmitters or between the receivers of the relay and source pairs are poor, such a collaboration is not beneficial.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".