18446320. ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA simplified abstract (QUALCOMM Incorporated)
Contents
ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA
Organization Name
Inventor(s)
Hamed Pezeshki of San Diego CA (US)
Taesang Yoo of San Diego CA (US)
ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18446320 titled 'ADAPTATION OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS BASED ON SITE-SPECIFIC DATA
Simplified Explanation
- Process for wireless communications at a network entity - Obtain site-specific data associated with a geographic location - Adapt machine learning model based on site-specific data - Generate updated machine learning model for estimating or predicting characteristics of wireless communications - Trigger event causes transmission of request for site-specific data - Trigger event based on location of network entity or change in physical characteristic of location
Potential Applications
- Improved wireless communication between network entities - Enhanced network performance based on site-specific data - More accurate estimation and prediction of wireless communication characteristics
Problems Solved
- Inaccurate estimation or prediction of wireless communication characteristics - Lack of adaptability in machine learning models for wireless communications - Limited performance optimization based on geographic location data
Benefits
- Increased efficiency in wireless communications - Better network performance and reliability - Enhanced user experience with improved connectivity and coverage
Original Abstract Submitted
Systems and techniques for wireless communications are described herein. For example, a process for wireless communications at a first network entity include obtaining site-specific data associated with a geographic location and adapting, at the first network entity, a machine learning model based on the site-specific data to generate an updated machine learning model for estimating or predicting of at least one characteristic associated with wireless communications between the first network entity and one or more network entities. The first network entity can experience a trigger event which causes the first network entity to transmit a request for the site-specific data. The triggering event can be based on at least one of a location of the first network entity in the geographic location or the first network entity moving to the geographic location or based on other factors such as a change in a physical characteristic of the location.
