Location and Allocation of Switching Equipment (Splitters/AWGs) in a WDM PON Network
Bibliographic record
Abstract
With the growing popularity of bandwidth demanding services such as HDTV, VoD, and video conferencing applications, there is an increasing demand on broadband access. To meet this demand, the access networks are evolving from the traditional DSL and cable techniques to a new generation of fiber-based access techniques. While EPONs and GPONs have been the most studied passive optical access networks (PONs), WDM-PON is more often seen as the next generation trend with an hybrid set of switching equipment. We propose a new optimization scheme for the deployment of greenfield WDM PON networks to minimize their total deployment costs. Based on the geographic location of ONUs and their corresponding traffic demand, our proposed scheme optimizes the placement of splitters and AWGs in a WDM PON. The solution process has two phases. In the first phase, ONUs are grouped around switching equipment into different clusters and then each cluster is assigned a passive equipment(i.e.,splitter/AWG). In the second phase, we develop a column generation (CG) algorithm based on a mathematical model, for selecting the best multi-stage placement equipment topology. The resulting combination of the clustering and of the column generation algorithms encompasses the particular cases where all switching equipment are splitters/AWGs/mixture and outputs the location of the switching equipment of the PON network. Numerical results allow the validation of the models and of the algorithms on various data sets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".