Performance study of opportunistic scheduling in dual‐hop multi‐user underlay cognitive network
Bibliographic record
Abstract
In this study, the authors investigate the performance of opportunistic scheduling for the dual‐hop amplify‐and‐forward multi‐user cognitive relaying network. Expressions are derived for the cumulative distribution function (CDF) and probability density function of the equivalent signal‐to‐noise ratio (SNR). From the derived CDF, the outage performance of the cognitive network is investigated. Then, an expression for average error probability is derived. Furthermore, simple and generic asymptotic expressions for the outage and error probabilities are obtained and discussed. In addition, a closed‐form expression for the system's ergodic capacity is derived. Their asymptotic results show that opportunistic scheduling has no impact on diversity gain. It is confirmed that the array gain determines the SNR advantage of opportunistic scheduling over the single‐user scenario. Moreover, they study adaptive power allocation under the total transmit power constraint in order to minimise the average error probability. As expected, the results show that optimum power allocation improves system performance compared with uniform power allocation. Finally, numerical results and Monte Carlo simulations are also provided to support the correctness of the analytical calculations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".