Design and performance analysis of distributed relay selection techniques in wireless networks
Bibliographic record
Abstract
We develop and analytically study the performances of three relay selection techniques for a two-hop decode-and-forward (DF) cooperative communication system in a large wireless sensor network with uniformly distributed nodes. It is assumed that there is no central processing unit to optimally select the relay and that the nodes use their locally available knowledge, that is, their distances from the source as well as their backward channel gains, to compete with one another to acquire the relaying status. The relay selection schemes are compared in terms of their fairness and energy efficiency. Taking into account both the fading effect and the nodes' locations distribution, the end-to-end outage probabilities of all relay selection schemes are derived and discussed. In particular, it is proved that when the source transmission power is high enough, the outage performance is independent from the scheme used to select the relay. Moreover, when the node intensity is large enough, the outage probability of the optimal relay selection scheme is almost the same as that of its suboptimal but more energy-efficient counterpart that uses the nearest node to the source as the relay. Computer simulations are used to validate the analytical results.
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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 | 0.003 | 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 | 0.000 |
| 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".