Proportional Fairness for MIMO Multi-user Schedulers with Traffic Arrival Process
Bibliographic record
Abstract
Packet scheduling at the data link layer may impact significantly the overall performance of a wireless system using multiple antennas. In this paper, we propose a novel packet scheduling scheme based on proportional fairness that considers the traffic arrival process with different packet lengths for the downlink of multiple-input multiple-output (MIMO) multi-user systems. We also provide analysis for the fairness of the new scheme in terms of time and service allocation. The scheduler, referred to as clock-time proportional fairness (C-T PF), performs at the packet level and can provide low average packet transmission delay as well as time and service fairness to users. It is work conserving and it can also take into consideration different users guarantees (heterogeneous users). We investigate an ideal service fair scheduler called C-T max-min for MIMO systems as well. We compare the performance of C-T PF with other MIMO schedulers. For the time and service fairness comparison of MIMO schedulers, we also propose time and service indexes. Simulations that consider the traffic characteristics and the mobility of users show the low average packet transmission delay and demonstrate the time and service fairness capabilities of C-T PF.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".