Lifetime Analysis of a Two-Hop Amplify-and-Forward Opportunistic Wireless Relay Network
Bibliographic record
Abstract
An expression is derived for the probability mass function (PMF) of the relay transmit power in a variable gain amplify-and-forward (VG-AF) opportunistic wireless relay network (OWRN). The PMF is used to calculate the average relay transmit power. An expression is also obtained for evaluating the transition probabilities between energy states in a Markov chain model of the OWRN. This model is used to compute the average OWRN lifetime for a small number of relays, allowable transmit power levels, and low initial relay energy levels. Unfortunately, the computational complexity of this approach becomes prohibitive as the number of relays, transmit power levels, and initial energy levels increase. A low-complexity method, based on an existing expression and the average relay transmit power, is used to estimate the average network lifetime. The method is shown to yield very accurate results for practical initial relay energy levels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".