Direct Learning Algorithm for Digital Predistortion Training Using Sub-Nyquist Intermediate Frequency Feedback Signal
Bibliographic record
Abstract
In this paper, a novel direct learning algorithm is proposed to identify the digital predistortion (DPD) coefficients that linearize a power amplifier (PA) using sub-Nyquist sampled intermediate frequency (IF) output of a heterodyne transmitter observation receiver (TOR). The learning algorithm is complemented with a joint time and phase alignment procedure to compensate for the unknown phase of the IF carrier as well as the delay between the PA input and output signals. By sub-Nyquist sampling at IF, the proposed method avoids the need for challenging receiver calibration that compensates for significant IQ imbalance exhibited by direct conversion receivers. Furthermore, it provides a very attractive flexibility in choosing the IF and consequently allows for a high subsampling factor. It is also extended to account for the nonflat frequency response of the TOR, thus avoiding the need for an explicit calibration step. Finally, measurement results were performed to linearize a PA demonstrator driven by a 320-MHz wide carrier aggregated LTE signal centered at 31 GHz using a complexity reduced Volterra-based DPD. Excellent linearization capacity (ACPR of 50 dBc and normalized mean square error of 2%) using significantly low sampling rates (as low as 40 Msps) is reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".