US 11,916,612 B2
Mobile terminal and communication quality prediction method
Riichi Kudo, Musashino (JP); Kahoko Takahashi, Musashino (JP); and Kohei Mizuno, Musashino (JP)
Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
Appl. No. 17/764,364
Filed by NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
PCT Filed Oct. 1, 2019, PCT No. PCT/JP2019/038691
§ 371(c)(1), (2) Date Mar. 28, 2022,
PCT Pub. No. WO2021/064848, PCT Pub. Date Apr. 8, 2021.
Prior Publication US 2022/0337329 A1, Oct. 20, 2022
Int. Cl. H04W 72/00 (2023.01); H04B 17/391 (2015.01); H04B 17/26 (2015.01); H04B 1/3827 (2015.01)
CPC H04B 17/3913 (2015.01) [H04B 1/3827 (2013.01); H04B 17/26 (2015.01)] 5 Claims
OG exemplary drawing
 
1. A mobile terminal for performing wireless communication, comprising:
a processor; and
a storage medium having computer program instructions stored thereon, when executed by the processor, perform to:
measure communication quality of the wireless communication;
generate terminal information consisting of at least one or more of a position, a posture, a motion, control information, camera and sensor information, and past communication information of the mobile terminal;
generate a communication quality model from the terminal information and the measured communication quality of the wireless communication using machine learning, thereby learning a relationship between communication quality of the wireless communication and the terminal information to predict communication quality from current terminal information;
estimate communication quality using the communication quality model;
determine an error between the measured communication quality and the estimated communication quality from the communication quality model;
generate a prediction deviation model from the terminal information and the error between the measured communication quality and the estimated communication quality using machine learning, thereby learning a relationship between the error and the terminal information to predict the error between the measured communication quality and the estimated communication quality from current terminal information; and
estimate the error between the measured communication quality and the estimated communication quality us the prediction deviation model.