A 28 GHz Dual-Mode Power Amplifier for Enhanced Load Resiliency or Back-Off Efficiency Enhancement in 22NM FDSOI Process
Bibliographic record
Abstract
This paper presents a dual-mode power amplifier (PA) with configurable gate biases, optimized for large-scale antenna array system (LSAAS) front-ends. In the first mode, the VSWR resiliency mode, the PA is configured to maximize resiliency to high load variations caused by antenna cross-coupling in LSAAS, by maintaining consistent power, linearity, and efficiency. In the second mode, the back-off (BO) efficiency enhancement mode, the PA is configured to improve BO efficiency when driven by complex modulated signals with high peak-to-average power ratio (PAPR), during minimal load variation. A 28 GHz circuit demonstrator is implemented using the 22 nm fully-depleted silicon-on-insulator (FD-SOI) CMOS process. Under a 50 -ohm load, the VSWR resiliency mode achieves 16.5 dB gain, 13 dBm output power, and 18 % peak power-added efficiency (PAE), while the BO efficiency enhanced mode delivers 14 dB gain, 13 dBm output power, with 17.5 % and 11 % PAE at peak and 6 dB back-off, respectively. When tested against a varying load with VSWR 2.5:1 over a 360° range, the VSWR resiliency mode shows an average saturated power loss of 0.5 dB, while the BO efficiency enhanced mode shows a 1 dB average loss. Modulated signal measurements further confirm the superior performance of the VSWR resiliency mode under load variation compared to the BO efficiency enhanced mode.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".