Direct Learning Algorithm for Digital Predistortion Training Using Sub-Nyquist Intermediate Frequency Feedback Signal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it