Experimental demonstration of non-iterative interpolation-based partial ICI compensation in100G RGI-DP-CO-OFDM transport systems
Bibliographic record
Abstract
We experimentally investigate the performance of a low-complexity non-iterative phase noise induced inter-carrier interference (ICI) compensation algorithm in reduced-guard-interval dual-polarization coherent-optical orthogonal-frequency-division-multiplexing (RGI-DP-CO-OFDM) transport systems. This interpolation-based ICI compensator estimates the time-domain phase noise samples by a linear interpolation between the CPE estimates of the consecutive OFDM symbols. We experimentally study the performance of this scheme for a 28 Gbaud QPSK RGI-DP-CO-OFDM employing a low cost distributed feedback (DFB) laser. Experimental results using a DFB laser with the linewidth of 2.6 MHz demonstrate 24% and 13% improvement in transmission reach with respect to the conventional equalizer (CE) in presence of weak and strong dispersion-enhanced-phase-noise (DEPN), respectively. A brief analysis of the computational complexity of this scheme in terms of the number of required complex multiplications is provided. This practical approach does not suffer from error propagation while enjoying low computational 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".