Experimental investigation of the equalization-enhanced phase noise in long haul 56 Gbaud DP-QPSK systems
Bibliographic record
Abstract
We experimentally demonstrate the impact of equalization-enhanced phase noise (EEPN) on the performance of 56 Gbaud dual-polarization (DP) QPSK long haul transmission systems. Although EEPN adds additional noise to the received symbols, we show that this reduces the phase variance introduced by the LO laser, and therefore should be considered when designing the carrier phase recovery (CPR) algorithms and estimating system performance. Further, we experimentally demonstrate the performance degradation caused by EEPN when a LO laser with a large linewidth is used at the receiver. When using a 2.6 MHz linewidth distributed feedback (DFB) laser instead of a ~100 kHz linewidth external-cavity laser (ECL) as a LO, the transmission distance is reduced from 4160 km to 2640 km due to EEPN. We also confirm the reduction of the phase variance of the received symbols for longer transmission distances showing its impact on the CPR algorithm optimization when a DFB laser is used at the receiver. Finally, the relationship between the EEPN-induced penalty versus the signal baud rate and the LO laser linewidth is experimentally evaluated, and numerically validated by simulations.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".