Optimal power‐aware relay placement for cooperative wireless sensor networks
Bibliographic record
Abstract
SUMMARY We study the problem of optimizing the symbol error probability (SEP) performance of cluster‐based cooperative wireless sensor networks. Recent studies in literature show that an efficient relay selection protocol based on simple geographical information of the nodes to execute cooperative diversity can significantly improve the SEP performance at the destination of such networks. As well, similar line of research on optimal power allocation (for the source and relay nodes) can be found in literature. However, to achieve the best SEP performance at the destination of a cooperative wireless sensor network, joint optimization of power allocation and relay placement should be accomplished. To this aim, we reformulate the SEP of a multi‐hop cooperative communication in a general form and optimize transmitted power level and relay placement simultaneously. This analysis is developed for both amplify‐and‐forward and decode‐and‐forward relaying protocols. Simulation results demonstrate that the proposed joint optimization can effectively improve the SEP performance of the network. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".