A Joint Transmission Grant Scheduling and Wavelength Assignment in Multichannel SG-EPON
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Bibliographic record
Abstract
We investigate the problem of grant scheduling in multichannel optical access networks using a scheduling theoretic approach. The network we consider is a novel cost-effective Ethernet Passive Optical Network (EPON) that is designed to operate with STARGATE or any evolutionary MAN. We show that the problem can modeled using an Open Shop model and we present a formulation for the joint scheduling and wavelength assignment problem as a mixed integer linear program (MILP) whose objective is to reduce the length of a scheduling period. Since the problem is shown to be NP-Hard, we introduce a tabu search based heuristic for solving the joint problem. Different other heuristics are also introduced and their performances are compared with those of tabu and MILP. Results indicate that by appropriately scheduling transmission grants and assigning wavelengths, substantial consistent improvements may be obtained in the network performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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