Heterogeneous Optical Access Networks: Enabling Low-Latency 5G Services With a Silicon Photonic Smart Edge
Bibliographic record
Abstract
In the 5G era, optical fronthaul is a major challenge in meeting growing demand. Edge computation and coordinated multipoint for 5G have stringent requirements for high throughput and low latency, either in single-wavelength or wavelength-division-multiplexing fronthaul. We propose a new silicon photonic solution to deliver 5G services on existing optical access networks with colorless optical network units, such as passive optical networks. The newly added 5G services form a heterogeneous optical access network. Using the existing fiber infrastructure, broadband services coexist with new 5G signals that can densify 5G coverage. The proposed scheme is both wavelength-selective (in the distribution network) and colorless (at the end user site). We use silicon microring modulators to create subcarriers slaved from the broadband service distributed carrier; additional microring modulators generate 5G signals exploiting those subcarriers. We experimentally validated the successful coexistence of 5G signals (various formats) with a broadband signal (various formats).
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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) | 0.001 | 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".