Uplink Scheduling in Wireless Networks with Successive Interference Cancellation
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we study the problem of uplink scheduling in wireless networks with successive interference cancellation (SIC). With SIC, concurrent transmissions, if properly scheduled, can be successfully decoded at a receiver. The scheduler decides: i. in which time-slot to schedule, and ii. in what order in a time-slot to decode each transmission in order to maximize the system utility and/or satisfy a system constraint. These two scheduling decisions effectively determine the rates allocated to concurrent transmissions, which in turn determine the throughput and fairness of the system. We consider several different scheduling problems in this context. The objective of the problems is to either maximize the throughput of the system or to obtain some kind of fairness among the users. We formulate and study each problem from the perspective of computational complexity. For each problem, we either propose a polynomial time algorithm, if any exists, or show that the problem is NP-hard.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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