US 12,250,658 B2
Utilizing machine learning models to estimate user device spatiotemporal behavior
Takai Eddine Kennouche, Meylan (FR); Christopher Michael Murphy, Bath (GB); and Howard John Thomas, Stonehouse (GB)
Assigned to VIAVI Solutions Inc., Chandler, AZ (US)
Filed by VIAVI Solutions Inc., San Jose, CA (US)
Filed on Mar. 25, 2022, as Appl. No. 17/656,633.
Prior Publication US 2023/0309053 A1, Sep. 28, 2023
Int. Cl. H04W 64/00 (2009.01); G01S 5/00 (2006.01); G01S 5/02 (2010.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01)
CPC H04W 64/003 (2013.01) [G01S 5/0063 (2013.01); G01S 5/0294 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); H04W 64/006 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment,
wherein the first type of data includes one or more of:
geographical mapping data corresponding to the mobile radio environment,
obstruction data corresponding to the mobile radio environment, or
event data identifying events corresponding to the user devices;
receiving, by the device, a second type of data identifying spatiotemporal behavior associated with the user devices of the mobile radio environment;
training, by the device, a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data;
training, by the device, a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model;
receiving, by the device, particular data identifying measurements associated with a user device and/or one or more base stations of the mobile radio environment;
processing, by the device, the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data; and
processing, by the device, the dimensionality-reduced spatiotemporal characteristic of the particular data, with the trained second model, to predict a spatiotemporal behavior of the user device.