US 12,328,604 B2
Utilizing invariant shadow fading data for training a machine learning model
Howard John Thomas, Stonehouse (GB); Christopher Michael Murphy, Bath (GB); Kexuan Sun, Stevenage (GB); Agustin Pozuelo, County Dublin (IE); Baruch Friedman, Dublin (IE); and Takai Eddine Kennouche, Meylan (FR)
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. 17/656,635.
Prior Publication US 2023/0308900 A1, Sep. 28, 2023
Int. Cl. H04W 72/02 (2009.01); G06N 5/022 (2023.01); H04W 16/22 (2009.01); H04W 24/02 (2009.01); H04W 24/08 (2009.01)
CPC H04W 24/02 (2013.01) [G06N 5/022 (2013.01); H04W 16/22 (2013.01); H04W 24/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area;
receiving, by the device, network topology data associated with the geographical area;
utilizing, by the device and based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data;
generating, by the device and based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will experience fading;
utilizing, by the device, the stochastic data to identify a realistic discoverable spatiotemporal signature;
training or evaluating, by the device, a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature;
generating a validation dataset for the system based on the realistic discoverable spatiotemporal signature; and
validating the system with the validation dataset.