Bibliographic record
Abstract
Current state-of-the-art long-haul lightwave communication systems operating at channel rates of 40 Gb/s or more are mostly limited by fiber nonlinearity. Nonlinear effects result in peculiar pattern-dependent interactions between optical pulses used for information transmission. Although pulses may interact in a different manner depending on the system design, this intersymbol interference cannot be overcome by simply increasing the signal power. On the contrary, it would only enhance the nonlinear effects and degrade the system performance even further. The method is applied to several channel models, both theoretical and practical. The most important of them are dispersion-managed soliton links and systems operating in the pseudolinear regime. The former are limited by pulse-to-pulse interactions, while the latter suffer from intrachannel four-wave mixing. In each case, a reasonable benchmark system is created first, and various related design aspects are discussed. It has been noticed that shorter optical pulses are generally less susceptible to intersymbol interference so a low duty cycle is preferable. Systems designed in such a way may have a lot of "white space" and are less attractive from the spectral efficiency point of view. For the constrained system, an ideal channel model is developed, and its connection with a physical fiber is established. Four particular runlength-limited coding schemes are constructed, and their performance is analyzed and compared against one another. Based on physical intuition and/or simulation results, specific properties that good codes must satisfy are pointed out. Most importantly, a potential improvement in channel capacity of up to 50% is demonstrated. In this work, it is shown that both problems can be solved by the application of constrained coding, an approach originally employed in magnetic recording systems. More specifically, certain constraints are imposed on transmitted sequences to increase the minimum pulse separation and mitigate the nonlinear interactions. Somewhat surprisingly, the data rate can be improved at the same time!An extension of this approach to multichannel long-haul systems is also considered in a separate chapter, and similar benefits are demonstrated with the help of numerical simulations.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".