Software-Defined Fiber Optic Communications for Ultrahigh-Speed Optical Pulse Transmission Systems
Bibliographic record
Abstract
We introduce the cognitive dynamic system (CDS) as the brain of software defined optical communications systems (SDOCS). The cyber processing layer of smart SDOCS based on CDS can be a good solution to control quality of transmission (QoT) by cognitive symbol decision-making and finding the best trade-off between transmission reach and capacity in nonlinear SDOCS. We modify the perceptor of CDS so that the posterior can be extracted directly without using the Bayesian equation to obtain a significant reduction of the CDS complexity. The low-complexity and simple algorithmic design of the proposed cognitive decision making (CDM) can make it suitable for SDOCS applications since CDS does not require the previous knowledge of the fiber optic transmission system parameters. Then, we experimentally demonstrate the performance enhancement of ultrahigh-speed optical pulse transmission system upgraded with the preceptor of CDS as the proof-of-concept of SDOCS. Experimental results show ∼1.3 dB enhancement in Q-factor for a 1.28 Tbaud (10 Gbaud × 128 OTDM) fiber optic system with polarization-multiplexed 64 quadrature amplitude modulation (QAM) at 15 Tbit/s data rate over a 150 km long fiber link. The CDS provides good reliability over system disturbances such as clock recovery intolerance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".