Asymmetric direct detection of orthogonal offset carriers assisted polarization multiplexed single-sideband signals
Bibliographic record
Abstract
We propose and demonstrate the asymmetric direct detection (ADD) of polarization division multiplexed single-sideband (PDM-SSB) signals with orthogonal offset carriers. ADD exploits the photocurrent difference to eliminate the Y-Pol interference in the X-Pol, and the X-Pol signal intensity to eliminate the X-Pol interference in the Y-Pol without resorting to iterative algorithms. This enables not only low-complexity signal linearization but also a simplified receiver front-end composed of a single optical filter, two single-ended photodiodes and two analog-to-digital converters (ADC). In the experiment, we first perform a parametric study of the proposed scheme at 40 Gbaud in the back-to-back configuration (B2B) to evaluate the performance impact of different system parameters including the carrier to signal power ratio (CSPR), the matched filter roll-off, and the filter guard band. Next, we demonstrate the transmission of 416 Gbit/s PDM 16-QAM signal over 80 km single-mode fiber (SMF) below the soft-decision forward error correction (SD-FEC) threshold of 2×10 −2 . We also numerically study the effectiveness of a 2×2 multiple-input-multiple-output MIMO equalizer in alleviating the inter-polarization linear crosstalk resulting from the non-orthogonal PDM-SSB signals due to polarization-dependent loss (PDL), which is not negligible for potential on-chip implementation of ADD.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".