A Fully Integrated 47.6% Fractional Bandwidth GaN MMIC Distributed Efficient Power Amplifier With Modified Input Matching and Power Splitting Network
Bibliographic record
Abstract
This article presents a fully integrated distributed efficient power amplifier (DEPA) for fifth-generation (5G) massive multiple-input multiple-output (MIMO) applications. In conventional DEPA, an individual input matching network (IMN), power splitter, and phase compensation line are used for each auxiliary power amplifier (PA) unit, resulting in a large overall size. To facilitate integration, an energy-efficient and compact DEPA with modified input matching and power splitting network is proposed, where a distributed input matching network (DIMN) is used for the auxiliary PA. A broadband input matching, reasonable power splitting, and phase alignment can be achieved simultaneously by adjusting the dimensions of gate transmission lines (TLs) between different auxiliary PA units. An integrated DEPA is implemented in a commercial 0.25- μm GaN-HEMT process to validate the proposed architecture. The fabricated DEPA shows a saturated output power of 40.4-41.7 dBm, an 8-dB back-off drain efficiency (DE) of 35%-50%, and a saturated DE of 46%-56% from 3.2 to 5.2 GHz, with a compact size of 3.4×3.0 mm2. Applying a 100-MHz orthogonal frequency division multiplexing (OFDM) signal with a 7.8-dB peak-to-average power ratio (PAPR), an average efficiency of 35.7%-47% is measured over the entire bandwidth, and the adjacent channel power ratio (ACPR) is better than -46 dBc after digital predistortion (DPD). To the best of our knowledge, the proposed DEPA demonstrates the widest bandwidth among all reported fully integrated back-off efficient PAs without using digital techniques or reconfiguration.
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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 | 0.001 |
| Open science | 0.001 | 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".