High Rate CV-QKD Secured Mobile WDM Fronthaul for Dense 5G Radio Networks
Bibliographic record
Abstract
A coherent transmission methodology for a continuous-variable quantum key distribution (CV-QKD) system based on quantum-heterodyne measurement through a coherent intradyne receiver is experimentally demonstrated in the framework of 5G mobile fronthaul links. Continuous optical carrier synchronization is obtained through training information, which is multiplexing to the quantum signal as pilot tone in both, frequency and polarization. Spectral tailoring by means of optical carrier suppression and single-sideband modulation is adopted to simultaneously mitigate crosstalk into the quantum channel and self-interference for the pilot tone, thus allowing for a high signal-to-noise ratio for this training signal. Frequency offset correction and optical phase estimation for the free-running local oscillator of the receiver is accurately performed and guarantees low-noise quantum signal reception at high symbol rates of 250 MHz and 500 MHz with additional Nyquist pulse shaping. A low excess noise in the order of 0.1% to 0.5% of shot-noise units is obtained for fiber-based transmission over a fronthaul link reach of 13.2 km. Moreover, co-existence with 11 carrier-grade classical signals is experimentally investigated. Joint signal transmission in the C-band of both, quantum signal and classical signals, is successfully demonstrated. Secure-key rates of 18 and 10 Mb/s are obtained under strict security assumptions, where Eve has control of the receiver noise, for a dark and a lit fiber link, respectively. Moreover, rates of 85 and 72 Mb/s are resulting for a trusted receiver scenario. These secure-key rates are well addressing the requirements for time-shared CV-QKD system in densified 5G radio access networks with cloud-based processing.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".