QPAR: A Quasi-Passive and Reconfigurable node for green next-generation optical access networks
Bibliographic record
Abstract
Passive optical network (PON) is regarded as a promising solution for the broadband bandwidth bottleneck problem. However, due to its passive nature, legacy PON is limited by the static power distribution, which makes it power inefficient. To address this problem, we propose QPAR [4], a Quasi-Passive and Reconfigurable node, which enables dynamic power and wavelength assignment so as to save optical power budget in PON. In this paper, we study the power gains that can be achieved in PON employing QPAR, as well as different factors that may facilitate or prevent real QPAR deployments. We conduct extensive simulations to demonstrate the merits of QPAR. Results show that QPAR can achieve high optical power saving by intelligently redistributing the unnecessary power assigned to “close” optical network units (ONUs) in the network. The saved power can either be used to connect more ONUs, or extend the network reach without increasing the optical power budget.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".