Broadband Optical Heterodyne Millimeter-Wave-over-Fiber Wireless Links Based on a Quantum Dash Dual-Wavelength DFB Laser
Bibliographic record
Abstract
We demonstrate real-time broadband multi-Gb/s electrical RF synthesizer-free millimeter-wave (MMW) signals generation and wireless transmission at the 5G new radio (NR) frequency band of 47 GHz based on analog radio-over-fiber (A-RoF) fronthaul. This is enabled by a low noise, highly correlated, monolithic C-band semiconductor InAs/InP quantum-dash (QDash) dual-wavelength distributed feedback (DW-DFB) laser. One laser mode is encoded using 4-/6-GBaud multilevel quadrature amplitude modulation (M-QAM) (16-/32-/64-QAM) baseband data signals, the other lasing mode is used as an optical local oscillator for optical-heterodyne remote up-conversion to a MMW carrier of 47.27 GHz. Consequently, optical baseband modulated data signals with data capacity up to 36 Gb/s (6-GBaud × 64-QAM) are transmitted through back-to-back (BtB) and 25-/50-km of standard single mode fiber (SSMF) before the MMW carrier is optically synthesized remotely for free space wireless data transmission and detection over up to 9-m. The end-to-end MMW-over-fiber (MMWoF) wireless link is thoroughly characterized exhibiting promising error-vector-magnitude (EVM) and bit-error-rate (BER) values. The 4-/6-GBaud 16-QAM MMWoF wireless links achieve EVMs down to 6.32%/7.33%, 6.71%/7.78%, and 7.35%/8.91% through BtB, 25-km, and 50-km SSMF, respectively. Similarly, the EVM for 32-QAM and 64-QAM links is observed to be 5.56%/6.56% and 6.05%/6.62%, respectively. Moreover, in each case, the calculated BER is below the forward error correction (FEC) limit of 3.8 × 10−3. The results corroborate the potential and viability of the QDash DW-DFB laser as a simple, efficient and cost-effective alternative to individual laser sources for deployment in broadband photonic MMWoF fronthaul systems of 5G wireless 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) | 0.001 | 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".