Per-user Throughput of Opportunistic Scheduling Scheme over Broadcast Fading Channels
Bibliographic record
Abstract
In this paper, we propose two analytical models for per-user throughput of an opportunistic scheduling scheme over a broadcast fading channel. For the first model, we use a piecewise linear approximation of the achievable transmission rates versus the values of Signal to Noise and Interference Ratio (SINR). We obtain the conditional average transmission rate of a mobile station, given the maximum channel quality of the other competing mobile stations. Using the probability distribution function of the maximum channel quality of the competing mobile stations, we obtain a closed form unconditional average transmission rate, i.e., per-user throughput, of a mobile station. For the second model, we use a similar approach, but with a precise model of the achievable rates. Furthermore, statistically nonidentical channels for different mobile stations are considered. Thus, the second model is more general and provides more accurate solution, but it requires more computations. The proposed models are useful for call admission control as well as performance studies of wireless networks. Simulation results are given to demonstrate the accuracy of the proposed analytical models.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".