A CNN-Based Digital Predistortion Method for Multi-Beam Phased-Array Transmitters
Bibliographic record
Abstract
This paper presents a multi-beam digital predistorter (MB-DPD) for phased-array transmitters. MB-DPD uses a convolutional neural network (CNN) to compensate for nonlinear distortions, hardware impairments, and cross-beam coupling over wide ranges of multiple beams’ steering angles, without the need for interpolation. By encoding steering angles’ information alongside the I/Q baseband signals of the main and neighboring subarrays, the proposed MB-DPD scheme mitigates both in-band and out-of-band distortions for any set of beam angles’ values that fall within the training angles’ ranges. Furthermore, MB-DPD uses transfer learning to reduce the need for retraining of the CNN layer hyperparameters as the steering angles’ ranges are altered or extended. Experimental validation with a 15 MHz signal at 3.5 GHz on a 20-element phased-array demonstrates that MB-DPD improves the normalized mean square error (NMSE) by about 10 dB across the beams’ ranges and significantly improves the adjacent channel power ratio (ACPR) by about 15 dB, and it is benchmarked against beam-oriented DPD (BO-DPD), and deep neural networks (DNN) to show the robustness and distortion mitigation of MB-DPD in various multi-beam setting conditions without any need for interpolation.
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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 | 0.001 |
| 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) | 0.000 | 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".