Digital Predistortion of Millimeter-Wave RF Beamforming Arrays Using Low Number of Steering Angle-Dependent Coefficient Sets
Bibliographic record
Abstract
This paper proposes a single-input single-output (SISO) digital predistortion (DPD) model for linearizing millimeter-wave (mm-wave) RF beamforming arrays. It starts with a dual-input power amplifier (PA) model that accounts for steering angle-dependent load modulation effects. This dual-input model is then transformed into a SISO model under the assumption of weak PA nonlinearity and RF beamforming architecture. The underlying coefficients of the SISO array model incorporate the beamforming weights, antenna cross-coupling, channel coefficients, and any possible phase-shifter gain variation with phase shift setting. An over-the-air (OTA) measurement setup is finally developed to validate the capacity of a SISO DPD model to linearize two different arrays-under-test with 4 and 64 elements and radiating mm-wave modulated signals with 320- and 800-MHz bandwidth. Although, in principle, the DPD coefficients should be retrained for each steering angle, experimental results have shown that the same set of coefficients can be used over a wide range of steering angles, and only a few sets of trained DPD coefficients are sufficient to minimize the distortion in a mm-wave RF beamforming array across a 120° steering range.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".