Time-variant entropy regulated multiple access for flexible coherent PON
Bibliographic record
Abstract
To further increase the data rate and rate flexibility of next-generation passive optical networks (PONs), we propose a scheme jointing probabilistic constellation shaping (PCS), constellation diagram identification, and coherent detection. The rate of the coherent PON is dynamically regulated with time-variant entropy, and monitored by the image classifier in real-time. The transmitted cascade frames allocated to different optical network units (ONUs) from the optical line terminal (OLT) can be synchronized, identified, and distinguished by the labeled entropy values. The proposed graphic monitoring scheme is experimentally demonstrated based on a dual-polarized coherent optical transmission system. A 0.5 interval value of entropy is precisely tuned to achieve a 96.13% constellation identification accuracy for entropy/rate by a fast normalized cross correlation coefficient (NCC)-based image classifier. A real-time data rate from 350 to 550 Gb/s with PCS-64-quadrature amplitude modulation (64-QAM) format for a single ONU is achieved which can maximally support five independent ONUs with single wavelength and two polarization states and the entropy varying from 3.5 to 5.5 with 0.5 intervals, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".