US Patent Application 18143337. ELECTRONIC DEVICE AND METHOD FOR GENERATING REPRESENTATIVE DATA FOR TRAINING BASE STATION MODEL simplified abstract
Contents
ELECTRONIC DEVICE AND METHOD FOR GENERATING REPRESENTATIVE DATA FOR TRAINING BASE STATION MODEL
Organization Name
Inventor(s)
Seungyeon Lee of Suwon-si (KR)
ELECTRONIC DEVICE AND METHOD FOR GENERATING REPRESENTATIVE DATA FOR TRAINING BASE STATION MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18143337 titled 'ELECTRONIC DEVICE AND METHOD FOR GENERATING REPRESENTATIVE DATA FOR TRAINING BASE STATION MODEL
Simplified Explanation
The patent application describes an electronic device with memory, a transceiver, and a processor that can perform various operations on base station data.
- The base station data is divided into smaller pieces based on a first time unit.
- The smaller pieces of base station data are combined to generate first data for the first time unit.
- The first data is further divided into multiple second time intervals based on a second time interval unit.
- Probability density functions are calculated for each second time interval.
- First representative data is generated using the probability density functions.
- The base station model is trained based on the first representative data.
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.
