Adaptive link selection in buffer-aided relaying with statistical QoS constraints
Bibliographic record
Abstract
This paper considers a 3-node buffer-aided relaying network with statistical delay quality-of-service (QoS) constraints imposed at the source and relay. To exploit the relay buffering capability and link fading diversity, an adaptive link selection relaying scheme is proposed. In a time slot, the relay (R) can adaptively select to receive from the source (S) or to transmit to the destination (D) based on the instantaneous conditions of the S-R and R-D links. The selection scheme aims to maximize the constant supportable arrival rate to the source, i.e., the effective capacity in consideration of the link fading distributions and the average signal-to-noise power ratios (SNRs) as well as the QoS constraints. We compare the capacities of the adaptive relaying and the fixed relaying where the relay employs fixed transmission and reception schedule, demonstrating the gain of the former, especially under loose QoS constraints. The capacities of the buffer-aided relaying and non-buffer relaying under similar end-to-end delay QoS constraint are also compared, showing the benefits of using buffer-aided relaying to support delay-sensitive applications.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".