Dual-Band Near-Field Probing Antenna for Enhancing the Performance of Dual-Band Shared-Aperture Linear-Polarized Phased Antenna Arrays
Bibliographic record
Abstract
This paper presents a novel dual-band, single-feed near-field probing antenna (NFP) designed to provide feedback signals for in-situ calibration and linearization of millimeter-wave dual-band, shared-aperture, linear-polarized phased antenna arrays. The NFP is designed to deliver flat coupling magnitudes and constant group delays to surrounding antenna elements across both frequency bands, while adhering to the tight$\lambda / 2$element spacing required at the high-frequency band. To validate its performance, a proof-of-concept prototype-a$2 \times 2$dual-band, shared-aperture, linear-polarized antenna array embedding the proposed NFP-was developed to operate within the$27-30 \text{GHz}$and$37-41 \text{GHz}$bands. S-parameter measurements confirmed the NFP's ability to effectively couple signals in both frequency bands, meeting the required group delay and frequency response constraints. Specifically, the measured coupling over 1 GHz segments (corresponding to the linearization bandwidth for a 200 MHz signal), the coupling magnitude variation remains below 1 dB across$26.5-28.5 \text{GHz}$and below 3 dB across 36-39 GHz. Furthermore, modulated signal measurements using two 200 MHz, 256-QAM OFDM signals demonstrated the NFP's capability in training a dual-band, NF-based digital predistortion (DPD) function. The DPD significantly enhanced the array's performance by compensating for non-linearities, achieving a 20 dB improvement in adjacent channel power ratio at 28 GHz and a 15 dB improvement at 38.5 GHz.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".