Invention Publication
- Patent Title: MACHINE LEARNING-BASED RADIO FREQUENCY (RF) FRONT-END CALIBRATION
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Application No.: US18188337Application Date: 2023-03-22
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Publication No.: US20230254003A1Publication Date: 2023-08-10
- Inventor: Lindsey Makana KOSTAS , Rishubh KHURANA , Ahmed YOUSSEF , Francisco LEDESMA , Sergey MURASHOV , Viral RANPARA , Enrique DE LA ROSA , Ming LEUNG , Gurkanwal Singh SAHOTA , Shahnaz SHIRAZI
- Applicant: QUALCOMM Incorporated
- Applicant Address: US CA San Diego
- Assignee: QUALCOMM Incorporated
- Current Assignee: QUALCOMM Incorporated
- Current Assignee Address: US CA San Diego
- Main IPC: H04B17/11
- IPC: H04B17/11 ; H04B17/21

Abstract:
Certain aspects of the present disclosure provide techniques and apparatus for calibrating radio frequency (RF) circuits using machine learning. One example method generally includes calibrating a first subset of RF circuit calibration parameters. Values are predicted for a second subset of RF circuit calibration parameters based on a machine learning model and the first subset of RF circuit calibration parameters. The second subset of RF circuit calibration parameters may be distinct from the first subset of RF circuit calibration parameters. At least the first subset of RF circuit calibration parameters is verified, and after the verifying, at least the first subset of RF circuit calibration parameters are written to a memory associated with the RF circuit.
Public/Granted literature
- US2170747A Apparatus for gauging wetted material Public/Granted day:1939-08-22
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