InAs/InP quantum dash buried heterostructure mode-locked laser for high capacity fiber-wireless integrated 5G new radio fronthaul systems
Bibliographic record
Abstract
We have developed and experimentally demonstrated a highly coherent and low noise InP-based InAs quantum dash (QDash) buried heterostructure (BH) C-band passively mode-locked laser (MLL) with a pulse repetition rate of 25 GHz for fiber-wireless integrated fronthaul 5G new radio (NR) systems. The device features a broadband spectrum providing over 46 equally spaced highly coherent and low noise optical channels with an optical phase noise and integrated relative intensity noise (RIN) over a frequency range of 10 MHz to 20 GHz for each individual channel typically less than 466.5 kHz and -130 dB/Hz, respectively, and an average total output power of ∼50 mW per facet. Moreover, the device exhibits low RF phase noise with measured RF beat-note linewidth down to 3 kHz and estimated timing jitter between any two adjacent channels of 5.5 fs. By using this QDash BH MLL device, we have successfully demonstrated broadband optical heterodyne based radio-over-fiber (RoF) fronthaul wireless links at 5G NR in the underutilized spectrum of around 25 GHz with a total bit rate of 16-Gb/s. The device performance is experimentally evaluated in an end-to-end fiber-wireless system in real-time in terms of error vector magnitude (EVM) and bit error rate (BER) by generating, transmitting and detecting 4-Gbaud 16-QAM RF signals over 0.5-m to 2-m free-space indoor wireless channel through a total length of 25.22 km standard single mode fiber (SSMF) with EVM and BER under 8.4% and 2.9 × 10 −5 , respectively. The intrinsic characteristics of the device in conjunction with its system transmission performance indicate that QDash BH MLLs can be readily used in fiber-wireless integrated systems of 5G and beyond wireless communication 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) | 0.000 | 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".