Experimental Demonstration of Multidimensional Voronoi Constellation With Two-Level Coding for Four-Core Fiber Transmission
Bibliographic record
Abstract
Multidimensional (MD) geometric shaping is an effective approach for achieving spectral efficiency gains in optical communication systems. MD formats also support the joint transmission across different cores of multi-core fibers (MCFs), enabling higher performance improvements through joint decoding. As a structured geometric shaping method, MD Voronoi constellations (VCs) allow low-complexity encoding and decoding without the need for look-up tables, offering superior performance over quadrature amplitude modulation (QAM) formats in terms of bit error rate and mutual information. Moreover, MD VCs can be combined with multilevel coding (MLC) schemes to achieve higher shaping gains after soft-decision (SD) decoding. In this paper, the performance of 16-dimensional VCs with MLC is experimentally demonstrated over a 50 km four-core weakly-coupled MCF transmission system. Compared to 16QAM with bit-interleaved coded modulation at the outer HD-FEC BER threshold of$ 4.5\times 10^{-3}$, VCs achieve a 6 dB reduction in the required launch power and extend the operating range by 17 dB.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".