Adaptive Multiple Relay Selection Scheme for Cooperative Wireless Networks
Bibliographic record
Abstract
In this paper, we propose an output-threshold multiple relay selection scheme for dual-hop multi-branch cooperative wireless networks. The proposed scheme selects the first L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> arbitrary ordered relays out of L relays such that the maximal ratio combined signal-to-noise-ratio (SNR) of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> relayed paths and the direct path barely exceeds a preset threshold. Closed-form expressions are derived for the cumulative distribution function, the probability density function, and the moment generating function of an output SNR upper bound for independent and identically distributed Rayleigh fading. Lower bounds for the outage probability, the average symbol error rate, and the average number of selected relays are also derived. Moreover, upper bounds for the average output SNR and the ergodic capacity are also derived. The analytical results are verified via the Monte-Carlo simulation. The performance of our proposed scheme is compared to that of the existing relay selection schemes. The proposed schemes provide more flexibility in utilizing bandwidth and spatial diversity in cooperative wireless networks.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".