Multisource buffer-aided relay networks: Adaptive rate transmission
Bibliographic record
Abstract
In this paper, we consider a multisource multirelay network where relays employ buffers to store the received user data packets before forwarding them to a common destination. The transmission schedule, i.e., when each source and each relay transmit, is not a priori fixed, but rather depends on the link qualities. In particular, we consider adaptive link selection and adaptive rate transmission for the considered network. For simple three node relay networks, it was shown before that buffer-aided relaying with adaptive link selection yields significant throughput gains compared to conventional relaying protocols using a fixed transmission schedule. In this work, we consider a general multisource multirelay framework where operations are more complex and analysis is more involved. First, we consider average sum rate maximization for adaptive rate transmission and derive an adaptive link selection policy which exploits the channel state information. As fairness is an important issue in multisource networks, we also consider max-min fairness constrained throughput optimization and derive the corresponding link selection policy. Numerical results show that the proposed link selection policies yield significantly higher throughputs compared to conventional relaying schemes where source and relay transmission schedules are a-priori fixed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".