Hybrid Harmonic Cancellation Digital Predistortion With a Feedback Loop Compensation
Bibliographic record
Abstract
High frequency (HF) transmitter whose operating frequency range is about four octaves is always faced with the problems of harmonics because of the nonlinearity of the power amplifier (PA). The conventional analog harmonic cancellation scheme uses lossy and bulky filter banks. The digital harmonic cancellation scheme requires a high sampling rate. In this brief, a hybrid harmonic cancellation digital predistortion (HHC-DPD) technique is proposed to suppress the interfering harmonics by adding a low-pass filter between the PA and the directional coupler. The proposed HHC-DPD scheme can greatly lower the sampling rate, in turn, decrease the digital power consumption and resource occupation. Further, a feedback loop compensation (FLC) method is proposed to cancel the time misalignment between the harmonics and the fundamental caused by the chromatic dispersion in the feedback loop. With the FLC method, the non-convergence issue in iterations can be avoided. Experimental results on an HF PA with carrier frequency located at 8 MHz validate that the proposed HHC-DPD scheme with the FLC outperforms current harmonic cancellation approaches. The sampling rate was reduced from 300 MHz to 100 MHz. Both the harmonics and the adjacent channel power ratio (ACPR) were cancelled for being below -50 dBc after the HHC-DPD.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".