Taking Turns With Adaptive Cycle Time a Decentralized Media Access Scheme for LR-PON
Bibliographic record
Abstract
The extended network span of next-generation long-reach passive optical networks (LR-PONs) results in extremely long propagation delays that severely degrade the performance of centralized bandwidth allocation algorithms. This is because these algorithms are based on bandwidth negotiation messages frequently exchanged between the optical line terminal (OLT) in the central office and optical network units (ONUs) near the users, which become seriously delayed when the network is extended. To address this problem, we propose a decentralized media access scheme for emerging LR-PONs to make the performance independent of the physical length between the OLT and ONUs. We also maintain centralized control over the network, usually missed in conventional decentralized schemes, to support and manage quality of service according to user service level agreements. The scheme thus combines decentralized media access with centralized control to meet the special requirements of emerging LR-PONs. We investigate the performance of the proposed scheme in contrast with centralized schemes under worst case conditions. We also explore various approaches to further enhance the performance of our scheme. Simulation results show that the average upstream packet delay can be decreased by 60% while also maintaining a high throughput.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".