InAs/InP quantum dash buried heterostructure mode-locked laser for high capacity fiber-wireless integrated 5G new radio fronthaul systems
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it