Bibliographic record
Abstract
We consider a dual-hop relay network with multiple source-destination (S-D) pairs and multiple relays, where amplify-and-forward relaying strategy is applied and transmission among S-D pairs takes place simultaneously. Network lifetime in this scenario is defined as the time interval over which successful transmission of all S-D pairs through selected relays can be maintained. We aim at designing relay selection to maximize the network lifetime for given data rate requirements of all the S-D pairs. Without knowledge of future channel states, we design relay selection algorithms to maximize perceived network lifetime at the current time. The perceived network lifetime maximization is shown to be a max-min optimization problem. We propose a priority search algorithm which is shown to provide the optimal solution with linear complexity in the number of relays. Furthermore, we propose a suboptimal priority-based selection strategy, the “worst-case” greedy algorithm, with complexity linear in the number of relays and quadratic in the number of S-D pairs. Simulation results show that the performance loss of the “worst-case” greedy algorithm is negligible as compared to the optimal relay selection solution.
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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 | 0.001 |
| 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".