Quality of Service in TDM/WDM Ethernet Passive Optical Networks (EPONs)
Bibliographic record
Abstract
Ethernet Passive Optical Network (EPONs) are currently being designed to deliver multiple services and applications, such as voice communications (VoIP), standard and highdefinition video (STV and HDTV), video conferencing (interactive video) and data traffic access network. The emergence of new bandwidth intensive applications and the continuous demand for more bandwidth in a bandwidth limited EPON require an upgrade from current TDM to WDM-based PON which is currently of huge interest in both the academia and industry. In this paper we propose three new Dynamic Bandwidth Allocation (DBAs) schemes for QoS support in WDM-based PON networks. These schemes can comply with any ONU architecture (tunable lasers or multiple fixed transceivers). However, they vary in their performances (i.e. different jitter, delay, bandwidth utilization etc.). We study the performance of these DBAs using extensive simulation experiments.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".