Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis
arXiv 2606.18395•fc200f1b76a55eadf8e6e90d3502f50f01ce1ebdf6783f80d91d12c56a73e053
artificial-noisecell-free-isaccnncovert-communicationdeep-learningdetection-error-probabilitydevice-to-device-d2ddigital-predistortiondoherty-power-amplifierelectro-optical-measurementgaN-HEMTgenetic-algorithmimperfect-csiintegrated-sensing-and-communicationinverse-designkl-divergencemicrowave-filtermulti-hopphysical-layer-securitypixelated-combinerrf-hardwarerotatable-antennarouting-metricwardenswireless
Paper metadata
- arXiv ID
- 2606.18395
- Version
- Not specified by this published record
- Category
- Electrical Engineering and Systems Science — Signal Processing (eess.SP)
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Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- fc200f1b76a55eadf8e6e90d3502f50f01ce1ebdf6783f80d91d12c56a73e053
- Enrichment time
- 2026-06-18T08:51:41Z
- AI-assisted enrichment
- Yes
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