Uplink Scheduling in Multi-Cell OFDMA Networks: A Comprehensive Study
Bibliographic record
Abstract
This paper proposes a comprehensive study of uplink scheduling in multi-cell OFDMA networks. We first focus on two scenarios for the homogeneous case, one without and one with a Cloud-RAN (C-RAN), and explore how to design efficient practical uplink schedulers for those scenarios. To compute the best achievable performance (BAP) under complete information, we study a centralized multi-cell scheduler. To this end, we formulate an MINLP problem and show how to solve it quasi-optimally. Then, we study the performance of an existing practical local benchmark scheduler (LBM) in terms of goodput and losses. We compare its performance to BAP and show that LBM only yields 44 percent of BAP. To reduce this performance gap, we propose two practical enhancements for LBM, one per scenario. The enhanced scheduler for the first scenario yields 51 percent of BAP (70 percent for the second). To reduce the gap further, we propose a new scheduler inspired by soft-frequency reuse (SFR). Its performance is 69 percent (resp. 83 percent) of BAP. It outperforms LBM by 56 percent for the scenario without C-RAN (84 percent with C-RAN). We finally extend our SFR-based scheduler to heterogeneous networks and show that it outperforms LBM by 53 percent for the scenario without C-RAN (96 percent with C-RAN).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".