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 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 | 0.001 |
| 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".