Dynamic Wavelength and Bandwidth Allocation in Hybrid TDM/WDM EPON Networks
Bibliographic record
Abstract
We discuss a wavelength-division-multiplexed-based passive-optical-network (PON) architecture that allows for incremental upgrade from single-channel time-division multiple-access PONs in order to provide higher bandwidth in the access network. Various dynamic-wavelength and bandwidth-allocation algorithms (DWBAs) for wave-division multiplexed PON are presented; they exploit both interchannel and intrachannel statistical multiplexing in order to achieve better performance, especially when the load on various channels is not symmetric. Three variants of the DWBA are presented, and their performance is compared. While the first variant incurs larger idle times (and, hence, poor performance), the other two algorithms achieve better but different performance with critical dissimilarities. Our analysis also focuses on the fair assignment of excessive bandwidth in the upstream direction to highly loaded optical network units. We compare the performance of DWBA to another algorithm that relies on static-channel allocation. Furthermore, a study is presented wherein the number of wavelengths increases, and a comparison with interleaved polling with adaptive cycle time is shown. We use extensive simulations throughout this paper
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".