NARMA-based linearization of RF power amplifiers with non-monotonic response under dynamic hardware reconfiguration
Bibliographic record
Abstract
This paper presents a signal processing block intended for a NARMA-based linearization of an RF power amplifier that exhibits a non-monotonic voltage response. This type of response may be obtained when applying a dynamic hardware reconfiguration of the amplifier for power efficiency improvement, through the activation or deactivation of sections in the RF transistor arrays or the electronic tuning of the bias circuits and the impedance matching circuits, as a function of the instantaneous envelope power of the modulated RF signal. A reconfigurable power amplifier design is described as an example of conditions that introduce a non-monotonic voltage response. The need, in that case, to introduce a new function inversion process for NARMA-based predistortion, is highlighted and explained. The proposed inversion process is described mathematically and validated through a power amplifier linearization example.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".