US Patent Application 18143437. METHOD AND ELECTRONIC DEVICE FOR BUILDING DIGITAL TWIN BASED ON DATA OF BASE STATION IN COMMERCIAL NETWOR simplified abstract
METHOD AND ELECTRONIC DEVICE FOR BUILDING DIGITAL TWIN BASED ON DATA OF BASE STATION IN COMMERCIAL NETWOR
Organization Name
Inventor(s)
Gyeongmin Pyeon of Suwon-si (KR)
METHOD AND ELECTRONIC DEVICE FOR BUILDING DIGITAL TWIN BASED ON DATA OF BASE STATION IN COMMERCIAL NETWOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 18143437 titled 'METHOD AND ELECTRONIC DEVICE FOR BUILDING DIGITAL TWIN BASED ON DATA OF BASE STATION IN COMMERCIAL NETWOR
Simplified Explanation
The patent application describes an electronic device with a memory, transceiver, and processor.
- The device receives base station data and divides it into smaller pieces according to a first time unit.
- It then combines these pieces to generate first data for the first time unit.
- The first data is further divided into smaller time intervals according to a second time interval unit.
- The device calculates probability density functions for each of these time intervals.
- It generates representative data using these probability density functions.
- Finally, the device uses the representative data to train a base station model.
Original Abstract Submitted
An electronic device includes a memory storing instructions, a transceiver configured to receive base station data, and at least one processor configured to execute the instructions to: divide the base station data into a plurality of pieces of base station data according to a first time unit; generate first data of the first time unit by superimposing the plurality of pieces of base station data on each other; divide the first data of the first time unit into a plurality of second time intervals, according to a second time interval unit; calculate at least one probability density function for each second time interval of the plurality of second time intervals; generate at least one first representative data by using respective probability density functions of the plurality of second time intervals; and train the base station model, based on the at least one first representative data.
