Dynamic resource allocation for delay-tolerant services in downlink OFDM wireless cellular systems
Bibliographic record
Abstract
The paper develops a framework for relating system performance to a user scheduling mechanism in downlink OFDM mobile cellular systems for delay-tolerant traffic. The performance of dynamic resource allocation techniques using best user (BU) and round robin (RR) strategies for user scheduling, and best sub-carrier assignment with power constraint and interference learning (BSA-PC-IL), is evaluated in terms of the fraction of satisfied users and system spectral efficiency (in kbps/MHz/cell). Simulation results indicate that in a low-mobility, single-cell environment, RR performs better than BU. However, in a high-mobility environment, BU significantly outperforms RR for both single-cell and multi-cell mobile systems. In a slow-mobility, multi-cell environment, BU has a slightly better performance than RR. In general, the BU scheme has a much slower degradation rate in the fraction of satisfied users at increased system loads than the RR scheme.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".