Fair and Efficient Scheduling in Wireless Networks with Successive Interference Cancellation
Bibliographic record
Abstract
This paper considers the problem of uplink scheduling in wireless networks supporting successive interference cancellation (SIC) at the physical layer. By allowing concurrent interfering transmissions, SIC enables multi-packet reception at the receiver resulting in increased network throughput. Specifically, we consider maximum throughput scheduling and proportional fair scheduling problems and study optimal and heuristic algorithms for these problems. We prove that the maximum throughput scheduling problem is NP-hard and develop a throughput efficient polynomial time greedy algorithm for the problem. While being throughput efficient, the maximum throughput scheduling can lead to highly unfair rates among the users. The proportional fair scheduling, on the other hand, is not throughput optimal but achieves proportional fairness among the users. For scheduling multiple users in a single time-slot, we show that there exists an algorithm that solves the proportional fair scheduling problem in polynomial time. For scheduling in multiple time-slots, we develop a greedy algorithm that computes a highly fair schedule in polynomial time. Numerical results are also provided to show the utility and efficiency of the proposed scheduling algorithms in various simulated networks.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".