A BROADBAND DUAL-INFLECTION POINT RF PREDISTORTION LINEARIZER USING BACKWARD REFLECTION TOPOLOGY
Bibliographic record
Abstract
This paper presents a ∞exible and generic broadband RF predistortion linearizer designed using backward re∞ection topology that can correct for the dual-in∞ection point type compression characteristics usually encountered in the gain proflle of metal semiconductor fleld efiect transistor (MESFET) based power ampliflers. It employs circuit conflguration of two parallel Schottky diodes with one p-intrinsic-n (PIN) diode in parallel, connected at two ports of a 90 - hybrid coupler. The Schottky diodes are coupled via a quarter wave transmission line segment which generates dual in∞ection points in the gain characteristics of the linearizer. The incorporation of a PIN diode helps in improving the achievable range in the gain and phase characteristics of the linearizer. Overall, the linearizer is capable of linearizing various types of power ampliflers owing to the ∞exible control on the linearizer's parameters and eventually the gain and phase characteristics of the linearizer. The proposed linearizer can be employed in the frequency range of 1.4{2.8GHz and can simultaneously improve the third- and flfth-order intermodulation distortions. The measurements carried out on a commercial ZHL-4240 gallium arsenide fleld efiect transistor (GaAs FET) based power amplifler demonstrates the broadband functionality of the proposed linearizer.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".