A Highly Linear and Efficient 28-GHz PA With a <i>P</i> <sub>sat</sub> of 23.2 dBm, <i>P</i> <sub>1 dB</sub> of 22.7 dBm, and PAE of 35.5% in 65-nm Bulk CMOS
Bibliographic record
Abstract
This article presents a 28-GHz power amplifier (PA) suitable for fifth-generation (5G) wireless systems. Designed in a 65-nm bulk CMOS, the PA achieves a saturated output power (Psat) of 23.2 dBm at 28 GHz. The AM-AM and the AM-PM distortion have the overshoot of less than 0.3 dB and 0.2°, respectively, at 28 GHz. The AM-PM variation remains less than 1.9° over the frequency band of 26-31 GHz with a peak power-added efficiency (PAE) of 35.5%. Several techniques, including strategic placement of varactors, a proposed multi-function coplanar-waveguide (CPW)-like power combining structure, and a systematic design approach for the passive networks, are used to improve the efficiency and linearity of the design leading to a negligible difference between P1dBand Psat. The proposed linearity improvement technique, which uses an adaptive feedback structure to adjust the input impedance of the output stage based on the output power and also direct more current at higher output power levels, enhances the linearity through lowering both AM-AM and AM-PM conversions. The design enables 2.5-GS/s 64-QAM operation at average Poutof 16.1 dBm with an error-vector magnitude/adjacent-channel power ratio (EVM/ACPR) of better than -26.2-dB/-28.32-dBc without using any digital pre-distortion.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".