Channel Power Optimization of WDM Systems Following Gaussian Noise Nonlinearity Model in Presence of Stimulated Raman Scattering
Bibliographic record
Abstract
The impact of interchannel stimulated Raman scattering (SRS) on optimization of channel powers to maximize the minimum channel margin is examined using a discrete Gaussian noise model for the Kerr nonlinearity. The simultaneous consideration of these two nonlinear effects is found to be incompatible with the goal of a convex SNR expression that can be optimized globally. A sequence of convex optimizations is employed to obtain a locally optimal solution, along with a bound on the degree of suboptimality. Optimization results obtained are most accurate for Gaussian-distributed signals, such as probabilistically shaped high-order-modulated signals. In a dispersion-uncompensated 4000-km fiber system utilizing the full C-band with perfect per-span SRS gain compensation, power optimization yields benefits of 0.25 to 2 dB over optimal spectrally flat power allocations. In systems including both C- and L-band, an optimization method that accounts for both SRS and Kerr nonlinearity effects provides a 0.23 to 0.60 dB margin benefit over a method compensating for SRS gain alone. In a system spanning only the C-band, per-span SRS gain compensation is not critical, as the maximum benefit is a 0.14 dB gain in minimum margin for optimized power allocations. By contrast, in a system spanning both C- and L-band, per-span SRS gain compensation provides a gain of up to 1.23 dB with optimized power allocations and larger gains with suboptimal power allocations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".