Auto-Encoder Optimized PAM IM/DD Transceivers for Amplified Fiber Links
Bibliographic record
Abstract
The use of semiconductor amplifier in integrated transceivers increases sensitivity, but changes the noise statistics in pulse amplitude modulation (PAM) intensity modulation with direct detection. Using a straight-forward, mixed noise model, we optimize constellations for these systems with an autoencoder-based neural network (NN). We improve required signal-to-noise ratio (SNR) by 4 dB for amplified spontaneous emission (ASE)-limited pulse amplitude modulation (PAM)4 and PAM8, without increasing system complexity. Performance can also be improved in O-band wavelength division multiplexing systems with semiconductor optical amplification and chromatic dispersion (CD). At 53 Gbaud, our simulations show we can extend the reach of PAM4 by 4 to 8 km when combining an optimized constellation with a NN decoder. We present an experimental validation of 4 dB improvement of an ASE-limited PAM4 back-to-back transmission at 60 Gbaud using an optimized constellation and a NN decoder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".