A Raman-Pumped Dispersion and Nonlinearity Compensating Fiber For Fiber Optic Communications
Bibliographic record
Abstract
An optical back propagation (OBP) technique using Raman pumped dispersion compensation fibers (DCF) is investigated to compensate for nonlinear impairments in WDM systems in real time. The proposed inline OBP module consists of an optical phase conjugator, amplifiers and a Raman pumped DCF. In order to suppress the nonlinear effects of the transmission fibers exactly, the power in the backpropagation fiber should increase exponentially with distance. This can be approximately achieved by using forward/backward Raman pumping of the dispersion compensating fiber (DCF). We introduce two configurations to realize the OBP. In this paper, we show that the OBP with forward/backward pumping provides 2.45 dB Q-factor gain compared to single-channel digital back propagation (DBP) when transmission distance is 1500 km for a WDM system with QAM-64. To minimize the variation of effective gain coefficient of the Raman pumped DCF as a function of distance, bidirectional pumping scheme which can provide the signal power profile closest to that required by the ideal OBP condition is proposed. The bidirectional pumping scheme provides a superior performance over forward/backward pumping and wideband DBP (i.e., DBP is applied on the entire WDM signal). Our numerical simulation results show that the bidirectional pumping scheme provides 7.6 dB and 5 dB advantage in Q-factor as compared to single-channel DBP and wideband DBP, respectively at a transmission distance of 5000 km. The maximum achievable reach of a long haul WDM system can be enhanced by 225% using bidirectional pumping scheme as compared to wideband DBP.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".