Phase-only predistortion for LINC amplifiers with Chireix-outphasing combiners
Bibliographic record
Abstract
Nonlinearities in linear amplification with nonlinear components (LINC) amplifiers using Chireix-outphasing combiners are studied and their sources are identified. A generalized analytical approach for finding phase-only predistortion functions to linearize these amplifiers is presented. Two distinct analytical solutions are found and are applied for phase-only linearization. The first solution consists of a static constant phase predistortion, which is introduced in one of the two RF amplifier branches. This constant phase is determined analytically as function of the value of the reactance of the stub used in the Chireix combiner. The second solution is a variable phase-distortion function, which is derived in explicit form. The two predistortion functions preserve constant envelope operation of the amplifiers. The impact of the two predistortion functions on the combiner efficiency is also investigated. It is found, through simulation with a code-division multiple-access signal and an experimental measurement with a 16 quadrature-amplifier-modulation signal that the two predistortion functions perfectly linearize the amplifier. It also shown analytically that, with perfect linearity, the efficiency of Chireix-outphasing amplifier is reduced and is, at best, equal to that of the LINC amplifier with a resistive combiner.
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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) | 0.001 | 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 | 0.000 | 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".