Reliability Benefit of Location-Based Relay Selection for Cognitive Relay Networks
Bibliographic record
Abstract
In this article, we develop an analytical framework to study the impact of location-based relay selection strategy on the reliability of cognitive relay networks. By utilizing the tool of stochastic geometry, we first derive a closed-form expression for the reliability-enhanced region (RER), where relaying transmission can achieve higher transmission reliability than direct transmission. Then, we adopt the normalized reliability gain (NRG) to quantify the reliability benefit obtained by using relaying transmission compared to direct transmission, and we obtain the spatial distribution of NRG in the RER. Subsequently, by taking the spatial random nature of relays’ distribution into account, we investigate the reliability benefit obtained by secondary networks with the optimal location-based relay selection (OLB-RS) strategy. To reduce the feedback overhead during relay selection, we propose a region-aware relay selection (RA-RS) strategy and obtain the achievable reliability benefit. The results indicate that the reliability is highly dependent on the location of relay, and the OLB-RS strategy is to select the relay closest to the midpoint between the corresponding secondary source and destination.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".