Delay analysis for ethernet long-reach passive optical networks
Why this work is in the frame
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Bibliographic record
Abstract
Designing low latency polling schemes is one of the most important parts for passive optical networks (PONs), particularly for long-reach PONs (LR-PON) which suffer from long propagation delays. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed transmission delay in LR-PONs. In this work, we evaluate three dynamic bandwidth allocation methods in terms of transmission delay. Namely, we consider conventional or interleaved polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We examine various flavors of each scheduling method and investigate their shortcomings and advantages in a LR-PON setting. Furthermore, we provide an analytical framework for obtaining packet delay in an enhanced version of RT-P method. The simulation results highly match the analysis for this framework. Also, our results indicate that RT-P method significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling methods.
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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 | 0.000 |
| 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