Nonlinearity-Aware Filtering and Constellation Design for the Fibre-Optic Channel
Bibliographic record
Abstract
We present two methods for reducing error rates in fibre-optic communications by taking into account the nonlinearity of the channel. For single-channel short-range links, we present a method for redesigning the output filter response using a new statistical tool which we describe in depth. For multi-channel long-haul fibres, we present a coding and modulation system that reduces inter-channel interference. In the single-channel short-haul case, we show that matched filtering at the outputwith a root-raised-cosine pulse is nonoptimal. We do this by constructing an output filter with improved performance. The new system includes out-of-band frequencies in the filter response in order to obtain lower-error results. We design this new filter response using a novel variation on principal component analysis that generates quasi-cyclic principal components. We name this new tool quasicyclic principal component analysis (QPCA), and additionally describe some of its nonfibre applications. In the multi-channel long-haul case, we construct a bounded-energy codebook that provides improved performance over existing schemes, not only through carefully designed symbol-level probabilistic shaping, but also through codeword-level shaping. This allows us to reduce inter-channel interference: specifically it gives an improvement of about 0.5 dB at the optimal operating point with simplified phase tracking. This performance improvement is reduced but not eliminated in the case of exact phase information at the output. In particular, the error-correction capabilities of our coding scheme are competitive with existing schemes, notably enumerative sphere shaping, while exhibiting improved runtime and space complexity.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".