Optimum Throughput Constrained Opportunistic Scheduling in Cellular Data Networks
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
This paper considers the problem of scheduling multiple users in the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling algorithms exploit the time-varying radio channel and improve the system performance. This paper introduces a new optimum scheduling algorithm that maximizes the sum of average user performance subject to certain minimum and maximum performance constraints. The proposed algorithm, which is named as throughput constrained opportunistic scheduling (TCOS), is an extension of the framework proposed by Liu et al. For memory less fading channels, TCOS performs identically to another optimum algorithm. For fading channels with memory, TCOS significantly improves system performance. The TCOS with the minimum and maximum performance constraints offers better service differentiation among different classes of users than opportunistic scheduling algorithm provides with only minimum performance constraints
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