Optimal power allocation in nonlinear MDM‐WDM systems using Gaussian noise model
Bibliographic record
Abstract
Abstract Mode‐division multiplexing (MDM) using few‐mode fibre (FMF) has received increasing attention to address the exponential growth of data traffic in long‐haul optical communication systems. Also, combining the MDM with wavelength‐division multiplexing (WDM) is a promising approach for dramatically growing the transmission capacity in such systems. However, a major barrier in this regard is the FMF nonlinear effects, which can significantly reduce the link performance. In this paper, in order to alleviate the FMF nonlinear effects, we focus on power allocation in FMF links by optimizing the input power of each optical WDM channel of each spatial mode, which leads to maximizing the total capacity transmission and also the minimum signal to noise ratio (SNR) margin. The FMF nonlinearity has been already modelled as the Gaussian noise (GN) for which no closed‐form formulation has been developed so far. Here, we derive a closed‐form GN model for this problem and verify it by comparing with the integral‐form GN model and split‐step Fourier method. In this approach, an optimal power is independently determined for each channel of each mode by optimizing a capacity maximization and a minimum SNR margin maximization problem in convex forms. The performance of different links including the single mode fibre‐WDM, MDM‐single channel, and MDM‐WDM are compared using computer simulations. These systems are comprehensively investigated in equal/non‐equal required SNR as well as flat/non‐flat amplifier gain scenarios. It is shown that optimized power allocation to each channel of each mode has a significant enhancement in the minimum SNR margin maximization scheme compared to the best equal power allocation. Furthermore, this improvement is much more in non‐equal required SNR and the non‐flat amplifier gain scenarios, showing the efficiency of the established approach in practical communication links.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".