Selection and placement of switching equipment in a Broadband Access Network
Why this work is in the frame
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Bibliographic record
Abstract
Motivated by the tremendous demands for cheaper and faster broadband access solutions, we propose a novel and optimal network design optimization scheme for a Broadband Access Network (namely, WDM PON. For a given geographical location of ONUs and their received/sent traffic demand, our proposed optimization scheme minimizes the WDM PON network deployment cost by generating the cost effective location of switching equipment. The solution scheme proceeds in two phases. In the first phase, ONUs are grouped into different clusters exploiting a hierarchical clustering heuristic. In the second phase, we develop a a mathematical model based on column generation (CG) algorithm which generates the minimum cost multi-stage placement equipment topology by selecting the best type and location of the switching equipment. The resulting combination of the clustering and of the column generation algorithms outputs the grouping of ONUS along with the type (either splitter or AWG) and the location of the switching equipment of the PON network. Computational results demonstrate the validation and effectiveness of the proposed solution scheme on various data sets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it