US 12,452,687 B2
Radio frequency plan generation for network deployments
Siddharood Halli, Bangalore (IN); Gopal Gupta, Bangalore (IN); Ajay Vishwanath Bhande, Bangalore (IN); and Charan Malyala, Bangalore (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP, Houston, TX (US)
Filed on Jan. 24, 2022, as Appl. No. 17/582,241.
Claims priority of application No. 202141056083 (IN), filed on Dec. 3, 2021.
Prior Publication US 2023/0180018 A1, Jun. 8, 2023
Int. Cl. H04W 16/18 (2009.01); H04W 24/02 (2009.01)
CPC H04W 16/18 (2013.01) [H04W 24/02 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A non-transitory machine-readable medium comprising instructions that, when executed by a processing resource of a computing device, cause the computing device to:
receive a reference radio frequency (RF) plan and an input RF plan of a network deployment area, wherein the reference RF plan indicates original access point (AP) locations and an original set of features of the network deployment area, wherein the input RF plan indicates a modified set of features of the network deployment area, wherein the original set of features and the modified set of features comprise at least spatial features and network features associated with the network deployment area, and wherein the network features comprise AP metrics of throuqhput or bandwidth;
extract, using an image segmentation process implemented by a first machine learning (ML) model, a group of pixels from the input RF plan;
label, using the first ML model, a feature of each group of pixels from the input RF plan;
generate, using the first ML model, an intermediate RF plan based on the modified set of features, the label of the feature of each group of pixels, and a first set of parameters of the first ML model, wherein the intermediate RF plan indicates candidate AP locations and the modified set of features;
provide, by the first ML model to a second ML model, the intermediate RF plan;
determine, using the second ML model, a network optimization score for the intermediate RF plan based on the candidate AP locations and the modified set of features;
determine, using the second ML model, an error value indicating a difference in the network optimization score for the intermediate RF plan and the network optimization score for the reference RF plan;
provide, by the second ML model to the first ML model, the error value;
in response to a determination that the error value is greater than a threshold, determine an optimized first set of parameters of the first ML model based on the error value; and
generate, using the first ML model, an output RF plan indicating optimized AP locations and the modified set of features based on the optimized first set of parameters.