Compensation Schemes for Transmitter- and Receiver-Based Pattern-Dependent Distortion
Bibliographic record
Abstract
A nonlinear pre-distorter (NLPD) at the transmitter, and a maximum-a-posteriori probability (MAP) detector, time-domain Volterra nonlinear equalizer (VNLE), or sparse-VNLE at the receiver are compared for compensation of the pattern-dependent distortion that can occur in high baud rate transmitters and receivers. Experimental results are presented for a 1.206-Tb/s dual-polarization 16-ary quadrature amplitude-modulation (16-QAM) superchannel signal with three subcarriers. The NLPD with iterative calculation of the pre-distortion provides the best performance in back-to-back systems and transmission systems followed by the MAP detector, VNLE, and sparse-VNLE. At the FEC threshold of 1.9 × 10-2, the NLPD provides an increase in the transmission distance of 300 km compared with the sparse-VNLE, VNLE, and MAP detector. For transmission over 1500 km of SMF, the VNLE and MAP detector exhibit additional optical signal-to-noise ratio margins of 0.16 and 0.47 dB relative to the sparse-VNLE. Compared with the VNLE, the sparse-VNLE exhibits a 55% reduction in number of kernel coefficients.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".