Bandwidth Distribution Solutions for Performance Enhancement in Long-Reach Passive Optical Networks
Bibliographic record
Abstract
Long-reach Passive Optical Networks (LR-PONs) aim to combine the capacity of metro and access networks by extending the reach and split ratio of the conventional PONs. LR-PONs appear as efficient solutions having feeder distances around 100km and high split ratios up to 1000-way. On the other hand, transmission of the signals in long distances up to 100km leads to increased propagation delay whereas high split ratio may lead to long cycle times resulting in large queue occupancies and long packet delays. Before LR-PON becomes widely adopted, the trade-off between the advantages and performance degradation problem which is resulting from long reach and high split ratio properties of LR-PONs needs to be solved. Recent studies have focused on enhancing the performance of dynamic bandwidth allocation in LR-PONs. This article presents a comprehensive survey on the dynamic bandwidth allocation schemes for LR-PONs. In the article, a comparative classification of the proposed schemes based on their quality-of-service awareness, base-types, feeder distances and tested performance metrics is provided. At the end of the article, a brief discussion on the open issues and research challenges for the solution of performance degradation in LR-PONs is presented.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".