Frequency Quadrupler for Millimeter-Wave Multiband OFDM Ultrawideband Wireless Signals and Distribution Over Fiber Systems
Bibliographic record
Abstract
Performance of millimeter-wave (mm-wave) multiband orthogonal frequency division multiplexing (MB-OFDM) ultrawideband (UWB) signal generation using a frequency-quadrupling technique and transmission over fiber is investigated by simulation and experiment, and the error vector magnitude (EVM) is used to evaluate the transmission quality. Frequency hopping within the first three bands of IEEE 802.15.3a has been used in experiments to obtain an MB-OFDM UWB signal. The frequency quadrupling can be achieved by using only one Mach–Zehnder modulator (MZM) and two cascaded MZMs. It is found that using one MZM is better than using two cascaded MZMs in the performance of mm-wave generation. For using one MZM, it is found that transmission through 20 km of fiber degrades the EVM by less than 0.5 dB compared with back-to-back. Also, the EVM is degraded by less than 1 and 0.25 dB for a bias drift of less than 20% and an extinction ratio of more than 10 dB, respectively. Moreover, the EVM of the system using one MZM is improved by more than 2.5 dB compared with the two cascaded MZMs. In addition, it is found that the minimum EVM required of −17 dB can be achieved by using a local oscillator modulation index of ∼63% and 70% at least for using the one MZM and two cascaded MZMs, respectively.
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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".