Digital Subcarrier Multiplexing: Enabling Software-Configurable Optical Networks
Bibliographic record
Abstract
The various topologies, traffic patterns and cost targets of optical networks have prevented the deployment of end-to-end solutions across multi-domains, and the optimization of the network as a whole. The consequent limitations in flexibility, scalability, and adaptability of optical networks will become increasingly important with new applications, such as 5G/6G. Coherent transceivers based on digital subcarrier multiplexing (DSCM) are proposed to address these current constraints. In particular, DSCM allows (i) the design of high-capacity point-to-point (P2P) and -multipoint (P2MP) optical networks; (ii) simplified aggregation with passive optics; and (iii) connections between low- and high-speed transceivers. Furthermore, DSCM-based networks reduce the number of opto-electro-opto stages, halve the number of bookended transceivers, and provide a better match for existing hub-and-spoke (H&S) traffic patterns in fast-growing and dynamic access/metro segments. A DSCM-based transceiver will pave the way for the deployment of next-generation flexible, adaptable, and scalable software-configurable optical networks. Key steps and elements to realize this solution are laid out, and promising applications outlined. The first real-time experimental results of coherent P2MP transceivers are presented.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".