Why this work is in the frame
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Bibliographic record
Abstract
Uplink scheduling in cellular networks is challenging due to power and interference management. Typically, each cell performs local scheduling, which requires estimation of inter-cell interference (ICI) to compute the appropriate modulation and coding scheme (MCS) based on the Signal-to-Interference-plus-Noise-Ratio (SINR) for each allocated resource block. Since schedules of neighboring cells are unknown to schedulers, the SINR can be badly estimated, which causes resource losses or under-utilization. The benchmark uplink scheduler we study in this paper produces a high goodput at the cost of significant resource losses, because it does not take the possibility of losses into account. Resource losses imply retransmissions, hence, high variability in delay. Therefore, a scheduler should be evaluated in terms of its goodput/loss trade-off. We propose a novel uplink scheduler that is inspired by Soft Frequency Reuse (SFR) and uses an MCS selection that takes the probability of losses into account, i.e., it selects an MCS that maximizes the effective rates seen by users, while keeping the loss probability below a threshold e. We show that the proposed scheduler yields significantly better goodput/loss trade-off than the benchmark scheduler.
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