US 12,395,961 B2
System and method for applying machine learning to mobile geolocation
Wing F. Lo, Plano, TX (US); and Imran Hafeez, Allen, TX (US)
Assigned to NetScout Systems, Inc., Westford, MA (US)
Filed by NetScout Systems, Inc., Westford, MA (US)
Filed on Sep. 2, 2022, as Appl. No. 17/902,262.
Claims priority of provisional application 63/398,028, filed on Aug. 15, 2022.
Prior Publication US 2024/0057013 A1, Feb. 15, 2024
Int. Cl. H04L 1/00 (2006.01); G06N 20/00 (2019.01); H04W 64/00 (2009.01)
CPC H04W 64/00 (2013.01) [G06N 20/00 (2019.01)] 24 Claims
OG exemplary drawing
 
1. A communication network monitoring device comprising a processor and a memory, wherein the memory comprises instructions that, when executed by the processor, cause the device to:
receive, from a base station (BS), radio predictors of a first user equipment (UE) for which a minimization of drive test (MDT) mode is activated, a UE location history of the first UE, a geolocation of the first UE, radio predictors of a second UE for which the MDT mode is not activated, a UE location history of the second UE, and cell physical parameters, wherein the radio predictors of the first UE comprise a timing advance (TA) of the first UE and a channel quality indication (CQI) of the first UE, and wherein the radio predictors of the second UE comprise a TA of the second UE and a CQI of the second UE;
train a machine learning (ML) model to determine an azimuth of the second UE at least based on the radio predictors of the first UE, the UE location history of the first UE, the geolocation of the first UE, and the cell physical parameters;
execute the ML model to determine the azimuth of the second UE; and
provide, to a downstream application, a geolocation of the second UE at least based on the azimuth of the second UE and the TA of the second UE.