Wideband RF power amplifier predistortion using real-valued time-delay neural networks
Bibliographic record
Abstract
this paper suggests the application of Real-Valued Time-Delay Neural Networks (RVTDNN) for Power Amplifier (PA) behavioral modeling and linearization. The Weights of the RVTDNN model and linearizer are identified using the Back Propagation Learning Algorithm (BPLA), which is applied to the measured input and output signals of the PA. The RVTDNN scheme is first successfully used to accurately predict the dynamic nonlinear behavior of a 250W LDMOS Doherty amplifier driven with 4 Carrier (4C) WCDMA signal. The RVTDNN is then applied for the construction of a digital predistortion to improve the linearity of the same Doherty amplifier. The ACPR of the linearized Doherty amplifier revealed an adjacent channel power ratio (ACPR) of better then 50dBc at the three offset frequency (5MHz, 10MHz, 15MHz).
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".