Code-division multiplexing for in-service out-of-band monitoring of live FTTH-PONs
Bibliographic record
Abstract
Feature Issue on Optical Code Division Multiple AccessWe propose, to the best of our knowledge, a novel in-service live fiber-to-the-home (FTTH) passive optical networks (PONs) management solution. Our solution uses a modified direct-sequence (DS) optical code-division multiplexing (OCDM) system and overcomes the optical time-domain reflectometry (OTDR) point-to-multipoint shortcomings. Our solution addresses various service provisioning and network maintenance challenges in PONs, alleviates their complexity, and reduces their operational cost. In addition, our system exploits passive devices (or encoders) to demark service provider ownership and responsibility from that of customers. Our OCDM-based management solution easily scales up from FTTH time-division multiplexing (TDM)-PON to WDM-PON and TDM/WDM-PON to support as many as a thousand customers, all using only one monitoring wavelength. We address the coding system and develop capacity curves for different PON scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".