Design of Impedance Matching Network for Low-Power, Ultra-Wideband Applications
Bibliographic record
Abstract
This paper addresses the design of ultra-wideband (UWB) impedance matching networks operating in the unlicensed 3.1–10.6 GHz frequency band for low-power applications. It improves the simplified real frequency technique (SRFT) by adding a realizability check and employing an iterative approach with different initial guesses in optimization to achieve realizable solutions under the requirements of UWB, low-power consumption, and a minimum number of circuit components. The comparison of solutions obtained using the SRFT with published solutions based on the Chebyshev filter theory is presented. It is shown that the optimal SRFT solution requires fewer components in the impedance matching network, maximizes the RF power delivery over the UWB spectrum with a reflection coefficient below −10 dB, and allows for circuit optimization to reduce power consumption. Using the improved SRFT, it demonstrates a systematic approach to find the strategies and limitations of designing the input matching networks for low-power UWB applications using GlobalFoundries 90 nm BiCMOS technology.
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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 | 0.000 |
| 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".