WLC10-6: Higher Layer Performance Study of Power-Controlled Hierarchical Constellation-Based Multi-user Opportunistic Scheduling
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 their earlier work, the authors proposed a multiple best user opportunistic scheduling using power controlled hierarchical constellations and showed that the proposed scheme offers more frequent information access (almost double) to the users at the expense of certain increase in the average transmit power. As follow-up of our earlier work, in this paper, we analyze higher layer performances of the proposed two best user scheduling scheme via queuing analysis. More specifically, we analyze the buffer distribution, the average buffer occupancy, the delay distribution, and the packet loss probability of the hierarchical constellation-based multiple best user scheduling which has been proposed earlier by the authors. We then compare these higher layer performances of the hierarchical two best user scheduling with the classical single best user scheduling. Selected numerical results show that the hierarchical two best user scheduling scheme outperforms the classical single best user scheduling in terms of buffer distribution, average buffer occupancy (i.e., average buffering delay), delay distribution, and packet loss probability of the packets at the transmission buffer at the expenses of an increase in the average transmit power.
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 | 0.000 | 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