Asymmetric Self-Coherent Detection Based on Mach-Zehnder Interferometers
Bibliographic record
Abstract
We propose an asymmetric self-coherent detection scheme (ASCD) based on Mach-Zehnder interferometers (MZI) for the field reconstruction of self-coherent (SC) complex double-sideband (DSB) signals. The MZI-ASCD scheme approaches the high electrical spectral efficiency (ESE) of homodyne coherent detection via a direct detection (DD) receiver having only two photodiodes (PD) and two analog-to-digital converters. The incoming SC-DSB signal is split into two parts at the receiver in this approach, one of which is delayed and beats with the other part at the outputs of an MZI. We show that the field reconstruction can be performed from the two tributaries of photocurrents. In addition, we present a modified MZI-ASCD scheme referred to as AUX-ASCD which introduces an auxiliary DD branch to improve the SNR of the detected signal. It is found that both the MZI-ASCD scheme and the AUX-ASCD scheme achieve higher OSNR sensitivity compared to the Kramers-Kronig scheme and in the meantime increases the ESE by a factor of 2 using a cost-effective DD receiver. These advantages make the ASCD scheme attractive for short-reach optical communications including edge cloud connections and mobile X-haul systems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".