Energy-Aware Cooperation Strategy With Uncoordinated Group Relays for Delay-Sensitive Services
Bibliographic record
Abstract
Due to channel fading and user mobility in wireless networks, quality-of-service (QoS) provisioning for multimedia services requires great effort. It is even more challenging to support the fast-growing multimedia services in a green manner. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission, and it can achieve energy saving. To support the delay-sensitive multimedia services in an energy-efficient manner, we consider a new framework in this paper where multiple source-destination pairs share a group of relays with an energy constraint. We also propose an effective uncoordinated cooperation strategy, which is based on the backoff timer. The theoretical performance bounds of the proposed strategy are derived with respect to the collision probability and the transmission success probability. As shown in the numerical and simulation results, the proposed strategy outperforms a probability-based uncoordinated strategy in terms of average packet delay, delay outage probability, and energy consumption. Further, we investigate the scalability of our proposed strategy and find that it can be deployed in a large-scale network.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".