US 12,493,790 B2
Generating variable communication channel responses using machine learning networks
Nitin Nair, Arlington, VA (US); Raj Bhattacharjea, Atlanta, GA (US); Tamoghna Roy, Arlington, VA (US); Timothy James O'Shea, Arlington, VA (US); and Nathan West, Washington, DC (US)
Assigned to DeepSig Inc., Arlington, VA (US)
Filed by DeepSig Inc., Arlington, VA (US)
Filed on May 27, 2022, as Appl. No. 17/827,250.
Claims priority of provisional application 63/194,744, filed on May 28, 2021.
Prior Publication US 2022/0383118 A1, Dec. 1, 2022
Int. Cl. H04B 17/391 (2015.01); G06N 3/08 (2023.01)
CPC G06N 3/08 (2013.01) [H04B 17/3912 (2015.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining one or more values indicating a channel response of a communication channel, wherein one or more values indicating the channel response is represented by a first data structure with N degrees of freedom;
providing the one or more values to a first machine learning model trained to generate parameters for a second data structure that is represented with less than N degrees of freedom;
obtaining a first output from the first machine learning model, the first output including one or more parameters;
generating the second data structure using the one or more parameters; and
providing the second data structure to a second machine learning model trained to generate a synthetic channel response that indicates power effects of a signal propagation within an environment.