Comparative Study of Two Architectures Suitable for the Generation of Wideband Signals at Sub-THz
Bibliographic record
Abstract
This paper presents a comparative study of two architectures for generating wideband modulated signals at sub-THz frequencies using a frequency bonding approach, focusing on achievable output signal quality. The first architecture generates a wideband signal at an intermediate frequency (IF) and up-converts it to sub-THz frequencies using a heterodyne mixer. The second approach generates multiple narrowband signals at IF, up-converts each to sub-THz, and then combines them. The study shows that, under linear up-converter operation, both architectures achieve similar signal-to-noise ratios (SNR) and are limited by the noise floor. However, replacing the combiner in the second architecture with a frequency duplexer improves the SNR by 3 dB. At higher IF power levels, where up-converter nonlinearity becomes significant, both architectures require digital predistortion to mitigate distortion. Despite this, the second architecture demonstrates superior adjacent-channel power ratio (ACPR). D-band measurements confirm that the second architecture enhances ACPR by up to 8 dB at high IF power levels when generating a modulated signal with a carrier frequency of 142.5 GHz and a modulation bandwidth of 1.2 GHz, while performance at lower power levels remains comparable.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".