A low‐spur harmonic‐cancelation digital predistortion based on neural network for frequency‐hopping HF transmitters
Bibliographic record
Abstract
Abstract High frequency (HF) transmitter implemented with the multioctave broadband power amplifier (PA) is always faced with the problem of interfering harmonics because of the nonlinearity of the PA. With the harmonic‐cancelation digital predistortion (HC‐DPD) scheme, the harmonics at lower frequencies can be canceled by the injected components and the harmonics at higher frequencies can be filtered out using a low‐pass filter (LPF). However, the HC‐DPD scheme brings the problem of unwanted spurs while lowering the requirements for sampling rate. In this article, a time‐delay neural network (TDNN) model is used to replace the conventional memory polynomial (MP) model in the forward modeling. Theoretical derivation and simulation results validate the effectiveness of the TDNN model in harmonic cancelation and spur suppression. Further, a frequency‐agile neural network (FANN) model is proposed based on the TDNN model. By adding the carrier frequency to the inputs of the network, the trained model can apply to all the trained carrier frequencies and is more friendly to the frequency‐hopping scenarios. Experiments were carried out on a 2–30 MHz HF transmitter testbench. Measurement results show that the harmonic‐to‐fundamental power ratio, the adjacent channel power ratio (ACPR), and the error vector magnitude (EVM) performances were all improved by more than 20 dB. Compared with the MP model, the unwanted spurs can be suppressed by up to 22 dB. In addition, the number of total model coefficients of the proposed FANN model is only 22% of that of the TDNN model under the frequency‐hopping scenario.
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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) | 0.000 | 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".