Nonlinearity Compensation for Next Generation Coherent Optical Fiber Communication Systems
Bibliographic record
Abstract
In this thesis, we focus on digital signal processing (DSP) solutions that can help to combat various sources of nonlinearity in fiber-optic communication systems. Fiber Kerr nonlinearities are widely known to constitute a fundamental limit on the capacity of long-haul optical transmission as they restrict the maximum launch power into the fiber. This effectively limits the maximum optical signal-to-noise ratio (OSNR) that can be achieved in the receiver which in-turn puts a cap on the transmission reach. However, cost-effective DSP-based fiber nonlinearity mitigation schemes for long-haul transmission are yet to be deployed. By employing Volterra analysis of an optical channel comprising multiple spans of single mode fiber (SMF), we develop two solutions to improve the complexity-performance trade-off of Volterra-based nonlinear equalization (VNLE). First, we demonstrate that a significant portion of the VNLE filter coefficients are canceled out in the presence of a symmetric dispersion map. Additionally, we propose novel cascade structures for VNLE that are shown to provide substantial complexity reductions compared to the conventional VNLE with linear and nonlinear filters in parallel. Note that other sources of nonlinear distortions can also significantly impair system performance. Recent experiments on transmission of high baud-rate optical signal revealed that the back-to-back performance of these systems are highly degraded by pattern-dependent distortion (PDD) which cannot be effectively compensated by linear filtering or static look-up-tables. In order to address these impairments, we developed a family of sequential detection strategies based on hidden Markov modeling of PDD. The proposed solutions are highly configurable to suit the target complexity constraints.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".