Comparison of Signal Generation Approaches for Component Testing at D-Band
Bibliographic record
Abstract
This paper presents a comprehensive comparison of three distinct methodologies for generating wideband signals within the D -Band frequency range $(110 \mathrm{GHz}$ to 170 GHz). The first method employs a high-speed Arbitrary Waveform Generator (AWG) to directly generate the Intermediate Frequency (IF) vector modulated signal. The second approach involves generating the In-phase and Quadrature (IQ) signal at baseband and utilizing an IQ mixer for upconversion to the IF. The third method utilizes multiple high-resolution AWGs for IF signal generation and employs channel bonding to achieve the desired wide bandwidth. Subsequently, a D-Band upconverter is utilized to upconvert the IF signal to the target D-Band frequency. A comparative measurement is conducted by generating an 8 GHz bandwidth Orthogonal Frequency Division Multiplexing (OFDM) signal at 9.6 GHz, which is then upconverted to 158.4 GHz. Results demonstrate that the first, second and third approaches yield an Error Vector Magnitude of $\mathbf{1 . 5 \%}, \mathbf{0 . 8 \%}$, and $\mathbf{1 \%}$, respectively. Furthermore, component testing is conducted by generating a 2.6 GHz bandwidth OFDM signal at the same frequencies, and linearizing a D-Band amplifier with an 8 GHz linearization bandwidth. The first, second and third approaches achieved an Adjacent Channel Power Ratio of -45.8/-49.1 dBc, -60.9/-63.8 dBc and -58.4/-62.4 dBc, 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".