Fiber nonlinear compensation and estimation for next generation optical networks
Bibliographic record
Abstract
Optical channel capacity is approaching the nonlinear Shannon limit with the assistance of advanced soft-decision forward error correction (FEC).For next generation optical systems, fiber nonlinearity effects are major impairments which limit the maximum capacity of backbone optical networks.In coherent transmission systems, low-complexity techniques to mitigate the fiber nonlinearities have attracted interest.In the optical network, a real-time fiber nonlinearity estimator would be essential in order to realize network optimization and throughput maximization.In this thesis we propose a low-complexity digital backpropagation (DBP) in subcarrier multiplexing systems (SCM) for fiber nonlinearity compensation.The technique is denoted as SCM-DBP.SCM-DBP can achieve a 50% maximum reach extension with 3200 km per DBP step for quadrature phase shift keying (QPSK) signals in single channel transmissions.Then, two modifications are proposed to further reduce the complexity of SCM-DBP.The first is reducing the number of interfering subcarriers when calculating the cross-subcarrier nonlinearity (CSN) distortion.The second is replacing the mathematical derived CSN filters with infinite impulse List of publicationsThe thesis work is based on eight publications with myself as the first-author, which includes four journal papers and four conference papers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".