Quantum dot multi-wavelength lasers for Tbit/s coherent communications and 5G wireless networks -INVITED
Bibliographic record
Abstract
We will present the design, growth, fabrication, electronic control and packaging of the InAs/InP quantum dot (QD) multi-wavelength lasers (MWLs) developed by AEP of NRC in Canada. Their key technical specifications include L-I-V curves, optical and RF beating spectra, relative intensity noise (RIN), and optical phase noise of each individual wavelength channel, as well as timing jitter are investigated. Data bandwidth transmission capacity of 5.376 Tbit/s and 10.3 Tbit/s respectively in the PAM-4 and 16-QAM modulation formats are demonstrated by only using a single QD MWL chip. We have also developed a novel monolithic InAs/InP QD dual-wavelength (DW) DFB laser as a compact optical beat source to generate millimeter-wave (MMW) signals. Because of using a common cavity, highly coherent and correlated optical modes with optical linewidth as low as 15.83 kHz, spectrally pure MMW signals around 46.8 GHz with a linewidth down to 26.1 kHz were experimentally demonstrated. By using this QD DW-DFB laser, a 1Gbaud (2Gbps) MMW over-fiber transmission link is demonstrated with PAM-4 signals. The results show that the demonstrated device is suitable for high speed high capacity MMW fiber-wireless integrated fronthaul of 5G networks.
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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) | 0.006 | 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".