Digitally Equalized Doherty RF Front-End Architecture for Broadband and Multistandard Wireless Transmitters
Bibliographic record
Abstract
This paper introduces a new architecture of frequency-agile Doherty power amplifier (PA)-based RF front-end. This architecture incorporates a baseband equalizer that is implemented using finite-impulse-response (FIR) digital filters to improve the performance of Doherty PAs when driven with large bandwidth and multiband wireless radios. Depending on the center frequency and bandwidth of the input signal, the FIR filters of the proposed equalizer are synthesized to compensate for the nonideal frequency behavior of the RF building blocks of the Doherty PA. It is shown that the proposed architecture enables the Doherty PA to operate with significantly improved power efficiency and linearity performance beyond its nominal frequency of design, which is suitable for multistandard applications. As a matter of fact, when driven with a 140-MHz bandwidth long-term evolution (LTE) signal centered around 2.30 GHz, the average efficiency of a 2.14-GHz Doherty PA prototype with the proposed digital baseband equalizer is enhanced from 33.5% to 44.7%. Moreover, its linearity performance in terms of adjacent channel leakage ratio (ACLR) is improved from -21 to -26 dB. In addition, when operated in concurrent dual-band mode with two 20-MHz bandwidth LTE signals centered at 1.96 and 2.34 GHz, the proposed Doherty PA enabled a reduction in dc power consumption by nearly 15%. Furthermore, it is demonstrated that the integration of the proposed baseband equalizer does not compromise the linearizability of the Doherty PA. To the best of the authors' knowledge, this is the first demonstration of the use of digital equalization for performance enhancement of RF Doherty PAs.
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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 | 0.001 | 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".