Convex Channel Power Optimization in Nonlinear WDM Systems Using Gaussian Noise Model
Bibliographic record
Abstract
Optimization of channel powers to maximize minimum margin or total capacity in WDM systems is studied. Using a Gaussian noise nonlinearity model, the signal-to-noise ratio (SNR) in each channel is expressed as a convex function of the channel powers. Using the SNR expression, convex optimization problems with objectives of maximizing the minimum channel margin or maximizing the fiber capacity minus a coding cap are formulated. Performance gains from software-based power optimization are observed in mesh networks and in point-to-point links having heterogeneous SNR requirements. By contrast, in systems with uniform amplifier noise and modulation formats, the optimized power allocation provides very little improvement over a traditional flat power allocation. In the 14-node NSFNET network, a margin gain of 1.5 dB on average is achieved through power optimization, as compared to a flat power allocation. Margin gains averaging 1.4 dB are found for subsets of this network with three to 13 nodes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".