Cost- and energy-efficient filterless architectures for metropolitan networks
Bibliographic record
Abstract
Network operators are forced to find cost- and energy-efficient solutions for networks supporting new and emerging services with strict latency and ultra-high-capacity requirements. A disruptive approach for delivering network agility in a cost- and energy-efficient manner is employing filterless optical networking based on broadcast-and-select nodes and coherent transceivers. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we investigate the performance of filterless optical networks in metropolitan core and aggregation networks where agility is required due to service dynamics, customer changes, and service flexibility requirements. We compare our results with a conventional metro network based on active switching. The results show that the filterless metro network based on a hierarchical structure similar to its active switching counterpart has comparable installed first cost and spectrum usage at 11 Tb/s of total traffic, as well as cost and wavelength consumption advantages of 19.5% and 16%, respectively, at 107 Tb/s of total traffic. These results confirm that the filterless architecture is an attractive alternative for metro network deployments.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".