Nonlinear modeling and digital predistortion for <scp>HF</scp> transmitters with harmonic cancelation
Bibliographic record
Abstract
Abstract High frequency (HF) transmitters implemented with the multi‐octave power amplifier (PA) are always faced with the problem of interfering harmonics because of the nonlinearity of the PA. In this paper, the harmonic cancelation digital predistortion (HC‐DPD) scheme is proposed and well discussed to cancel the in‐band intermodulation distortion (IMD) as well as out‐of‐band harmonics at the same time. With the digital filter‐less scheme, the bulky and lossy filter banks can be removed. Nonlinear models are first utilized to approximate the nonlinear behavior of the HF PA and compared in terms of the model accuracy. Then, the learning architecture, model identification, and feedback compensation of the HC‐DPD scheme are detailed discussed. Finally, a series of simulations and experiments demonstrated the effectiveness of the proposed nonlinear models and linearization algorithms.
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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 | 0.000 |
| 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 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".