A Scalable Large-Array M-QAM Direct-RF Transmitter Topology With Integrated Physical Layer Security—A Proof of Concept
Bibliographic record
Abstract
This article introduces a transmitter (TX) array topology that employs QPSK direct-RF TX units for realizing scalable large-array wireless communication and multifunction systems. By leveraging spatial power combination, the TX units are set to synthesize an extended M-QAM constellation, enabling flexible and high-order modulation. The QPSK modulation, when transmitted in an array, bolsters physical layer security by confining the constellation retrieval to the intended radiation angle, exhibiting high selectivity. Additionally, its low-dynamic-range (DR) waveform supports high-power output and improves the efficiency of power amplifiers (PAs) compared to higher-order constellations, thus significantly enhancing the array’s overall power–performance ratio. A comprehensive analysis is conducted on key performance factors, including location-dependent antenna gain variations, beamforming effectiveness, and phase front flatness. To validate the proposed technique, a$2\times 4$array proof-of-concept (PoC) implementation is presented. Measurement results demonstrate robust performance across modulation orders ranging from 16-QAM to 256-QAM, aligning with theoretical predictions. This topology effectively integrates spatial constellation formation with the requirements of large-array systems, offering superior power efficiency. Its scalable building blocks, reconfigurability, and inherent secure communication capabilities make it a strong candidate for next-generation high-data-rate large-array TX systems.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".