Quantum-Dot Multi-Wavelength Lasers for Millimeter Wave Generation and Transmission
Bibliographic record
Abstract
In order to achieve ultrahigh data capacity and to overcome the wireless spectrum crunch, 5G is going to adopt millimeter-wave (mmW) frequencies (30 GHz - 300 GHz). To generate high-quality mm W signals by lasers, it requires optical sources with ultra-narrow optical linewidth and low relative intensity noise (RIN). In this paper, we have developed InAs/InP quantum dot (QD) multi-wavelength lasers (MWLs) around 1550 nm with the frequency spacing from 10 GHz to 1000 GHz. Those QD MWLs have very low RIN, ultra-narrow optical linewidth, small timing jitters, compact size, low power consumption and the ability for hybrid integration with silicon substrates. As an example, we present a buried heterostructure (BH) QD dual-wavelength (DW) DFB laser as an optical beat source for mmW generation. The BH QD DW-DFB laser with the optical linewidth of 16 KHz and the RIN of -158 dB/Hz is capable of generating spectrally pure mm W signals between 46 GHz and 48 GHz. By using it, we have demonstrated a real time 24-Gbit/s (64QAM x 4Gbaud) data bandwidth wireless transmission operating at 47.2-GHz carrier over 25-km SSMF.
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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) | 0.001 | 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".