US 12,439,271 B2
Machine-learning-based wireless planning using antenna radiation patterns
Serkan Isci, Plainsboro, NJ (US); Krystian Czapiga, Hillsborough, NJ (US); Yaron Kanza, Fair Lawn, NJ (US); James Klosowski, Ridgefield, CT (US); Velin Kounev, Weehawken, NJ (US); and Gopalakrishnan Meempat, East Brunswick, NJ (US)
Assigned to AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed by AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed on Nov. 1, 2022, as Appl. No. 18/051,622.
Prior Publication US 2024/0147251 A1, May 2, 2024
Int. Cl. H04W 16/28 (2009.01); H04W 16/18 (2009.01)
CPC H04W 16/18 (2013.01) [H04W 16/28 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
creating, by a processing system including at least one processor, a geospatial model of an environment in which a cellular network is to be deployed;
transforming, by the processing system for each cellular antenna of a proposed antenna layout of the cellular network, a radiation pattern of the each cellular antenna into a signal strength array, to create a plurality of signal strength arrays;
augmenting, by the processing system for each signal strength array of the plurality of signal strength arrays, the each signal strength array with at least one parameter of a corresponding cellular antenna of the proposed antenna layout and at least one value describing the environment in which the cellular network is to be deployed; and
estimating, by the processing system, a coverage of the proposed antenna layout based on the signal strength array, as augmented, using a machine learning model.