Enhancing O-Band PAM Performance With a CNN at Equal Complexity to an FIR Filter
Bibliographic record
Abstract
Multiple-level intensity-modulation with directdetection (IM/DD) is a cost-effective solution for increasing capacity within data centers and other short haul links. Conventional equalizers, typically based on linear filtering, are designed under Gaussian noise assumptions; amplified IM/DD links have non-Gaussian statistics. Neural network (NN) solutions can adapt naturally to both standard (Gaussian) and amplified (non-Gaussian) IM/DD systems. In contrast to previous research, we examine neural network implementations that incur no increase in complexity over simple, finite impulse response filtering. Comparing standard filtering with our NN solution, our experiments with a silicon photonic modulator in the Oband show gains from 0.25-0.75 dB in Q-factor across multiple wavelengths and fiber lengths. For example, a net rate of 100 Gb/s (post-KP4-FEC overhead) can be achieved at 1300 nm over 3.4 km. We achieved additional gain with a modified four-level constellation adapted to channel statistics. The same-complexity NN solution can be used for amplified IM/DD systems employing low-cost integrated semiconductor optical amplifiers (SOAs), such as metro/5G fronthaul links, and emerging multiplexing architectures with a shared laser and SOA.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".