Physical Layer Service Fairness of 200-Gbps TDM-NOMA Coherent Passive Optical Networks Within Single Timeslot
Bibliographic record
Abstract
As coverage increases or network topology becomes more complex, the optical path loss difference between any two access points in tree-like passive optical networks (PON) is uneven, causing service fairness problems at the physical layer. The service fairness of the far-near user access in 200-Gbps coherent PON with various user cases is discussed in this work. A fairness allocation mechanism based on time division multiplexing (TDM) non-orthogonal multiple access (NOMA) in the time-power domain is proposed theoretically to provide fair transmission between the arbitrary two optical network units (ONUs) with the best and the worst performance within single timeslots. Then, a series of subsequent experiments are performed with different path loss and split ratios (SR) to prove the effectiveness and practicality of the proposed NOMA scheme, totally including 12 subcases. Any sub-experiment proves that by optimizing power allocation, the signal-to-interference noise ratio (SINR) or bit-error rate (BER) on the physical layer can be approximated to the same value. The Jain's fairness index at a fixed and flexible hard-decision forward error correction (HD-FEC) standard, and modified fairness index are used to evaluate service fairness in different cases. This TDM-NOMA scheme may address the service fairness issue in next-generation coherent PON with wide coverage.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".