Per-user Throughput of Opportunistic Scheduling Scheme over Broadcast Fading Channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it