Time-Domain Analog Front-End for UWB WLAN Transform-Domain Receiver
Bibliographic record
Abstract
This paper addresses the problem of the time-domain direct-sequence front-end for transform-domain ultra-wideband (UWB) wireless local area network (WLAN) receiver. The proposed architecture comprises a single-ended voltage-voltage dynamic feedback low-noise amplifier (LNA), quadrature mixer, and baseband filter. The dynamic feedback with inductive output load reduces the LNA to a simple second-order filter, with zero at the origin, while improving the conversion gain (CG), noise figure (NF). Thus, the CG is further maximized when limiting the two poles within the 5-6 GHz frequency band. The mixer, based on a merged quadrature topology, employs single-peak notch network, with benefits to the CG, NF, and IIP3. The front-end, drawing 10, 9 mA form 1.8 v, achieves at 5.6 GHz 34.8 dB conversion gain, 12.1 dB noise figure, and 1-dB gain desensitization with -8 dBm interferer power at 7 GHz. Other simulation results are -2.35 dBm minimum IIIP3, and -35 dBc rejection at the UWB groups #1 and #3.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".