A neural network based on-line adaptive predistorter for power amplifier
Bibliographic record
Abstract
In this paper, we present a real-time linearizing technique based on Real Valued Tapped Delay Neural Network (RVTDNN) for base band signal predistortion of Power Amplifier (PA). The proposed architecture is suitable for adaptive linearizing of PAs with memory effects. Instead of using indirect learning architecture, we propose a data on-line adaptive predistortion to ensure a continuous adaptation without interrupting the transmitting process. With the proposed architecture, a reliable transmitting process is permanently ensured. The compensation is global including a memory non-linear PA, the modulator-demodulator, and A/D and D/A converter imperfections. The adaptation algorithm minimizes a given cost-function, considered as the mean square error (MSE) between the input Cartesian (I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IN</sub> , Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IN</sub> ) components and those of the PA output divided by the maximum realizable linear gain. About 30dBc in ACPR improvement is achieved with quick convergence and good stability.
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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".