Experimental Demonstration of 600 Gb/s Net Rate PAM4 Transmissions over 2 km and 10 km with a 4-λ CWDM TOSA
Bibliographic record
Abstract
We propose to introduce a controlled amount of inter-symbol interference (ISI) during pulse shaping in a high symbol rate intensity modulation direct detection (IM/DD) system to increase the alternating current (AC) power of the received electrical signal after photo-detection and transimpedance amplification. We experimentally demonstrate high-speed 4-level pulse amplitude modulation (PAM4) transmissions with a 4-λ coarse wavelength division multiplexing (CWDM) transmitter optical sub-assembly (TOSA) using the proposed scheme. The intentionally introduced ISI is mitigated using either transmitter-side Tomlinson Harashima precoding (THP) or receiver-side feed forward equalization (FFE) aided with a 2-tap post filter and maximum likelihood sequence estimation (MLSE). Both methods enable 4 × 81 Gbaud PAM4 transmission over 2 km of single mode fiber (SMF), with a bit error rate (BER) below the 7% overhead hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10-3, which corresponds to a net rate of >600 Gb/s. We also explore the limit of our system at 10 km. The result shows that an aggregated net rate of 600 Gb/s excluding the HD-FEC overhead can still be achieved by loading different symbol rates onto different wavelength channels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".