Limiting Properties of Overloaded Multiuser Wireless Systems With Throughput-Optimal Scheduling
Bibliographic record
Abstract
Throughput-optimal scheduling has been widely discussed due to its capability to stabilize single-hop multiuser wireless systems if possible. However, most of the previous discussions focused on the underloaded scenario, i.e., the arrival rate lies inside the achievable rate region. The behavior of throughput-optimal scheduling in overloaded multiuser wireless systems is the focus of this paper. We first show that, with the infinite buffer assumption, although all the queues are unstable, both the average throughput and a function of queue length converge as time evolves. In addition, the average throughput is the solution to a convex optimization problem whose objective is determined by the scheduling algorithm. By investigating the average throughput of two special throughput-optimal scheduling algorithms, i.e., the generalized MaxWeight and Log-Rule, we find that users can be prioritized by tuning the parameters associated with the scheduling algorithm, but the fairness is not likely to be guaranteed and some users may starve. Second, by studying the finite buffer system, we show that whether the buffer is dedicated to each queue or shared among queues has a great impact on the system performance, and the potential user starvation problem can be alleviated by a proper design.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".