Thermal memory effects modeling and compensation in RF power amplifiers and predistortion linearizers
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Bibliographic record
Abstract
Memory effects, which influence the performance of RF power amplifiers (PAs) and predistortion-based linearizers, become more significant and critical in designing these circuits as the modulation signal bandwidth and operation power increase. This paper reports on an attempt to investigate, model, and quantify the contributions of the electrical nonlinearity effects and the thermal memory effects to a PA's distortion generation, as well as how to compensate for these effects in designing baseband predistortion schemes. The first part of this paper reports on the development of an accurate dynamic expression of the instantaneous junction temperature as a function of the instantaneous dissipated power. This expression has been used in the construction of an electrothermal model for the PA. Parameters for the new proposed behavior model were determined from the PA measurements obtained under different excitation conditions (e.g., small-signal and pulsed RF tests). This study led us to conclude that the effects of the transistor self-heating phenomenon are more important under narrow-band signal (e.g., enhanced data for global evolution of global system for mobile communications) than for signals with wide modulation bandwidth (CDMA2000, Universal Mobile Telecommunications System). In the second part of this paper, the newly developed model has also been used to design a temperature-compensated predistortion function to compensate for these effects. The linearized PA output spectrum and error vector magnitude show a significant performance improvement in the temperature-compensated predistortion function over a memoryless predistortion. The results of these measurements that have been conducted on a 90-W peak lateral double-diffused metal-oxide-semiconductor PA are in agreement with those obtained from simulations using the developed PA and the predistorter models implemented in an ADS environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it