Digitally Equalized Doherty RF Front-End Architecture for Broadband and Multistandard Wireless Transmitters
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 it