Linearisation of radio frequency power amplifiers exhibiting memory effects using direct learning‐based adaptive digital predistoriton
Bibliographic record
Abstract
This study presents an adaptive predistortion algorithm based on direct learning approach to compensate for the non‐linearities of a power amplifier (PA) exhibiting memory effects. The proposed algorithm implements the steepest descent technique on an odd‐order memory polynomial model to optimise the predistorter coefficients. The performance of the proposed algorithm is validated using a harmonically tuned broadband PA driven by long‐term evolution 20 MHz signal. Measurement results confirmed the robustness of the proposed technique by adapting the coefficients of the predistorter to the changes in average input power, drain bias, and gate bias of the PA. The linearisation using the proposed algorithm is compared to the traditional uncompensated case and results are presented. For changes in average input power, gate bias and drain bias levels, the normalised mean square error shows substantial enhancement when the predistortion coefficients are updated using the proposed algorithm.
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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 | 0.000 |
| 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 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".