Array Calibration and Digital Predistortion Training Using Embedded Near-Field Feedback Probes and Orthogonal Coding for Enhancing the Performance of Millimeter-Wave Beamforming Arrays
Bibliographic record
Abstract
This letter proposes an active array calibration and digital predistortion (DPD) training method that relies on a series of measurement data captured using near-field (NF) probes embedded within the array to enhance the performance of millimeter-wave (mm-wave) radio frequency (RF) beamforming arrays. These measurements are obtained using phase settings that are based on orthogonal coding to enable the characterization of the linear and nonlinear errors in the array’s RF chains. The use of embedded NF probes in the proposed method makes it suitable for infield testing. Specifically, the proposed theory is formulated to allow for beamforming-phase-dependent error calibration as well as array linearization without resorting to element-wise measurement or far-field (FF)-based feedback. Furthermore, the proposed theory does not impose a flat coupling requirement between the NF probes and the array antenna elements. Experimental results are conducted on a custom-built 16-element RF beamforming array with four embedded NF probes and operated at 37.5 GHz. The measurements revealed that applying the proposed calibration method reduced the imbalance in the radiation pattern side lobes by up to 2 dB and achieved comparable performance to element-wise FF-based calibration. Furthermore, the proposed DPD training method enabled increasing the effective isotropic radiated power (EIRP) from 32 to 34.2 dBm while maintaining an error vector magnitude (EVM) below 3.5%.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".