An Artificial-Intelligence (AI) Assisted Mm-Wave Doherty Power Amplifier with Rapid Mixed-Mode In-Field Performance Optimization
dc.contributor.author
Wang, Fei
dc.contributor.author
Xu, Shaojie
dc.contributor.author
Romberg, Justin
dc.contributor.author
Wang, Hua
dc.date.accessioned
2022-06-07T06:56:23Z
dc.date.available
2022-06-02T13:11:36Z
dc.date.available
2022-06-07T06:56:23Z
dc.date.issued
2019
dc.identifier.isbn
978-1-7281-3143-6
en_US
dc.identifier.isbn
978-1-7281-3144-3
en_US
dc.identifier.other
10.1109/IMC-5G47857.2019.9160368
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/550547
dc.description.abstract
This paper presents an artificial-intelligence (AI) assisted “intelligent” millimeter-wave (mm-wave) Doherty power amplifier (PA) architecture that achieves robust adaptive operation over antenna VSWR. A built-in machine-learning core utilizes online function estimation and reinforcement learning algorithms for dynamic Doherty performance optimization. As a proof of concept, a 3-bit mixed-signal Doherty PA (MSDPA) at 28GHz is used as a hardware platform to demonstrate the performance improvement with the AI core. Over 2:1 antenna VSWR variation, simulations using extracted Doherty PA schematic show up-to 4.9 dBm P 1dB improvement and up-to 7% PAE improvement at P 1dB .
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Artificial intelligence (AI)
en_US
dc.subject
Doherty
en_US
dc.subject
Efficiency
en_US
dc.subject
Linearity
en_US
dc.subject
Machine learning (ML)
en_US
dc.subject
Mixed-signal
en_US
dc.subject
Power amplifier (PA)
en_US
dc.subject
Power back-off (PBO)
en_US
dc.subject
Reinforcement learning
en_US
dc.title
An Artificial-Intelligence (AI) Assisted Mm-Wave Doherty Power Amplifier with Rapid Mixed-Mode In-Field Performance Optimization
en_US
dc.type
Conference Paper
dc.date.published
2020-08-06
ethz.book.title
2019 IEEE MTT-S International Microwave Conference on Hardware and Systems for 5G and Beyond (IMC-5G)
en_US
ethz.pages.start
9160368
en_US
ethz.size
3 p.
en_US
ethz.event
2019 IEEE MTT-S International Microwave Conference on Hardware and Systems for 5G and Beyond (IMC-5G)
en_US
ethz.event.location
Atlanta, GA, USA
en_US
ethz.event.date
August 15-16, 20219
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09757 - Wang, Hua / Wang, Hua
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::09757 - Wang, Hua / Wang, Hua
en_US
ethz.date.deposited
2022-06-02T13:11:41Z
ethz.source
BATCH
ethz.eth
no
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-06-07T06:56:30Z
ethz.rosetta.lastUpdated
2024-02-02T17:23:02Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=An%20Artificial-Intelligence%20(AI)%20Assisted%20Mm-Wave%20Doherty%20Power%20Amplifier%20with%20Rapid%20Mixed-Mode%20In-Field%20Performance%20Optimization&rft.date=2019&rft.spage=9160368&rft.au=Wang,%20Fei&Xu,%20Shaojie&Romberg,%20Justin&Wang,%20Hua&rft.isbn=978-1-7281-3143-6&978-1-7281-3144-3&rft.genre=proceeding&rft_id=info:doi/10.1109/IMC-5G47857.2019.9160368&rft.btitle=2019%20IEEE%20MTT-S%20International%20Microwave%20Conference%20on%20Hardware%20and%20Systems%20for%205G%20and%20Beyond%20(IMC-5G)
Dateien zu diesem Eintrag
| Dateien | Größe | Format | Im Viewer öffnen |
|---|---|---|---|
|
Zu diesem Eintrag gibt es keine Dateien. |
|||
Publikationstyp
-
Conference Paper [35344]