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Deep learning advances Doherty power amplifier design with novel synthesis method

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for designing Doherty power amplifiers using deep learning, specifically deep convolutional neural networks (CNNs) combined with genetic algorithms (GA) and dual-state impedance synthesis. This approach addresses the complex integration of load modulation, impedance matching, and phase compensation within the output combiner. Prototypes fabricated using this methodology achieved a saturated output power exceeding 44.2 dBm and a peak drain efficiency above 71.2% in the 2.6-2.8 GHz range, with a drain efficiency of 64% at a 6-dB back-off level. After digital predistortion, adjacent channel leakage ratios better than -51.3 dBc were recorded. AI

IMPACT This research could lead to more efficient and powerful wireless communication systems by improving amplifier performance.

RANK_REASON The cluster contains a research paper detailing a new technical methodology for designing electronic components. [lever_c_demoted from research: ic=1 ai=0.7]

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Deep learning advances Doherty power amplifier design with novel synthesis method

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The cluster contains a research paper detailing a new technical methodology for designing electronic components. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Han Zhou, Haojie Chang, David Widen, Christian Fager ·

    Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis

    arXiv:2606.18395v1 Announce Type: cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging. In this paper, we propose a …

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