Resource Allocation and Relay Selection for Collaborative Communications
Bibliographic record
Abstract
We investigate the relay selection problem in a network where users are able to collaborate with each other; decode and forward the messages of each other along with their own messages to the destination. We study the performance obtained from collaboration in terms of (1) increasing the achievable rate, (2) saving the transmit energy, and (3) reducing the resource (time-bandwidth) requirement. To ensure fairness, we assume that the transmit energy to the rate ratio is fixed for all users. We allocate resource optimally for the proposed collaborative protocol (CP) and compare the result with the non-collaborative protocol (NCP) where users transmits their messages directly to the destination. The collaboration gain allows us (1) to decide whether to collaborate or not and (2) to select one relay among the possible relay users. We show that a considerable gain can be obtained if the direct source-destination channel gain is significantly smaller than those of alternative links. We demonstrate that a rate and energy improvement of up to (1+n√(k/k+1)) can be obtained, wherekis the environment path loss exponent and k is the ratio of the rates of involved users. We also show that the collaboration is only beneficial for the middle range rate ratio.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".