US 12,294,878 B2
Wireless communication system, monitoring station, defect detection method, and wireless communication program
Toshiro Nakahira, Musashino (JP); Hirantha Abeysekera, Musashino (JP); Shoko Shinohara, Musashino (JP); Koichi Ishihara, Musashino (JP); and Yasushi Takatori, Musashino (JP)
Assigned to Nippon Telegraph and Telephone Corporation, Tokyo (JP)
Appl. No. 17/796,142
Filed by Nippon Telegraph and Telephone Corporation, Tokyo (JP)
PCT Filed Jan. 28, 2020, PCT No. PCT/JP2020/002979
§ 371(c)(1), (2) Date Jul. 28, 2022,
PCT Pub. No. WO2021/152693, PCT Pub. Date Aug. 5, 2021.
Prior Publication US 2023/0121752 A1, Apr. 20, 2023
Int. Cl. H04W 56/00 (2009.01); H04L 43/0823 (2022.01); H04W 24/08 (2009.01)
CPC H04W 24/08 (2013.01) [H04L 43/0823 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A wireless communication system comprising:
a plurality of base stations to which one or more terminal stations are connectable; and
a monitoring station configured to monitor each of the plurality of base stations,
wherein the monitoring station comprises:
an information collection unit, implemented using one or more computing devices, configured to collect, from each of the plurality of base stations, wireless environment information including a plurality of information items indicating wireless environments around the plurality of base stations and the one or more terminal stations;
a defect detection unit, implemented using one or more computing devices, configured to detect whether or not one or more defects occur based on the plurality of information items; and
a defect determination unit, implemented using one or more computing devices, configured to determine one or more estimated occurrence probabilities of the one or more defects detected by the defect detection unit based on the plurality of information items,
wherein the defect detection unit is configured to detect whether or not the one or more defects occur by performing machine learning of making a classification into a defect class or a non-defect class for each defect item indicating details of a defect based on the plurality of information items, and
wherein the defect determination unit is configured to determine the one or more estimated occurrence probabilities of defects by performing machine learning on the respective one or more defects detected by the defect detection unit for each defect item based on the plurality of information items.