Regenerator Placement in Survivable Optical Networks Using Deep Tensor Neural Network
Bibliographic record
Abstract
With the rapid adoption of the Internet of Things and mobile devices, the global Internet traffic is estimated to increase by nearly three times from 2017 to 2022. Hence, the demand for higher bit rates is necessary to support massive data transfers over optical networks. Ideally, a network that is able to handle many long distance requests at high transmission speeds is preferable. However, given limited spectrum resources, a trade-off is inevitable when selecting the correct routing parameters. On the one hand, we can minimize the usage of resources using spectrally efficient modulation formats. As a result the effective transmission distance of the signal will be reduced. Similarly, if we are to use a low-level modulation format, we can increase the effective range of transmission, but the amount of the used spectrum will also increase. It is in these scenarios where the importance of regenerators become apparent. When the link distance between two nodes is too large, a regenerator is required to regenerate the signal strength. However, regenerators are expensive to install. Additionally, given that the regenerator number needs to be set during the design of the network architecture, efficient calculation of their allocation is required to reduce each node's limitations, thus optimizing the network CAPEX and OPEX. In this paper, we demonstrated the feasibility of using deep tensor neural network to optimize regenerator placement in optical networks. Our approach brings significant improvements over the results achieved by the currently deploying techniques and can be used in the real-life networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".