Granted Patent B2

US 12,739,669 · App. 18/531,974 · Granted Sep 15, 2026

Inventor: Wenyuan Qi (Shanghai, CN)

Assignee: GM GLOBAL TECHNOLOGY OPERATIONS LLC

H04W24/06G06N20/00

Abstract

Claims (57)

1 . A method for estimating signal to interference and noise ratio (SINR) in a wireless network environment, the method comprising:

collecting a plurality of wireless connection datasets about the wireless network environment, wherein each of the plurality of wireless connection datasets includes at least one measured environment parameter and a measured SINR, wherein collecting the plurality of wireless connection datasets further comprises:

receiving the plurality of wireless connection datasets from one or more wireless devices, wherein the at least one measured environment parameter of each of the plurality of wireless connection datasets includes at least one of: a channel quality indication (CQI) value, a weather condition value, and an environment complexity value;

generating a regression model based at least in part on the plurality of wireless connection datasets, wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter; and

training an SINR optimization machine learning algorithm to determine an optimized estimated SINR based at least in part on the estimated SINR, wherein training the SINR optimization machine learning algorithm further comprises:

determining a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and

training the SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide the optimized estimated SINR as an output.

2 . The method of claim 1 , wherein the environment complexity value quantifies at least one of: a number of obstacles and an average height of obstacles which are obstructive to wireless transmissions.

3 . The method of claim 2 , wherein the environment complexity value is determined by each of the one or more wireless devices using an environment complexity identification machine learning model.

4 . The method of claim 1 , wherein the one or more wireless devices includes at least: a vehicle equipped with a vehicle communication system.

5 . The method of claim 1 , wherein training the SINR optimization machine learning algorithm further comprises:

training the SINR optimization machine learning algorithm to account for edge cases using a plurality of simulated wireless network environments.

6 . The method of claim 5 , wherein training the SINR optimization machine learning algorithm to account for edge cases using a plurality of simulated wireless network environments further comprises:

generating a plurality of simulated wireless network environments using a computer simulation; and

training the SINR optimization machine learning algorithm based at least in part on the plurality of simulated wireless network environments.

7 . The method of claim 6 , wherein generating the plurality of simulated wireless network environments further comprises:

initializing the plurality of simulated wireless network environments, wherein each of the plurality of the simulated wireless network environments is defined by at least one simulated environment parameter;

adding a plurality of simulated wireless nodes to each of the plurality of simulated wireless network environments to form a plurality of simulated wireless network environments; and

measuring one or more simulated SINRs within each of the plurality of simulated wireless network environments.

8 . The method of claim 7 , wherein training the SINR optimization machine learning algorithm based at least in part on the plurality of simulated wireless network environments further comprises:

determining one or more estimated SINRs for each of the plurality of simulated wireless network environments using the regression model based at least in part on the at least one simulated environment parameter of each of the plurality of simulated wireless network environments;

comparing the one or more estimated SINRs to the one or more simulated SINRs for each of the plurality of simulated wireless network environments to determine a plurality of simulated SINR deviations; and

training the SINR optimization machine learning algorithm based at least in part on the at least one simulated environment parameter of each of the plurality of simulated wireless network environments and the plurality of simulated SINR deviations.

9 . A system for estimating signal to interference and noise ratio (SINR) in a wireless network environment, the system comprising:

a plurality of wireless nodes; and

one or more central computers in electrical communication with the plurality of wireless nodes, wherein the one or more central computers are programmed to:

collect a plurality of wireless connection datasets about the wireless network environment using the plurality of wireless nodes, wherein each of the plurality of wireless connection datasets includes at least one measured environment parameter and a measured SINR, wherein to collect the plurality of wireless connection datasets, the one or more central computers are further programmed to:

receive the plurality of wireless connection datasets from the plurality of wireless nodes, wherein the plurality of wireless connection datasets are collected by one or more wireless devices in wireless communication with the plurality of wireless nodes, wherein the one or more wireless devices includes at least: a vehicle equipped with a vehicle communication system, and wherein the at least one measured environment parameter of each of the plurality of wireless connection datasets includes at least one of: a channel quality indication (CQI) value, a weather condition value, and an environment complexity value;

generate a regression model based at least in part on the plurality of wireless connection datasets, wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter; and

train an SINR optimization machine learning algorithm to determine an optimized estimated SINR based at least in part on the estimated SINR, wherein to train the SINR optimization machine learning algorithm, the one or more central computers are further programmed to:

determine a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and

train the SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide the optimized estimated SINR as an output.

10 . The system of claim 9 , wherein to train the SINR optimization machine learning algorithm, the one or more central computers are further programmed to:

train the SINR optimization machine learning algorithm to account for edge cases using a plurality of simulated wireless network environments.

11 . The system of claim 10 , wherein to train the SINR optimization machine learning algorithm to account for edge cases using the plurality of simulated wireless network environments, the one or more central computers are further programmed to:

generate a plurality of simulated wireless network environments using a computer simulation; and

train the SINR optimization machine learning algorithm based at least in part on the plurality of simulated wireless network environments.

12 . The system of claim 11 , wherein to generate the plurality of simulated wireless network environments, the one or more central computers are further programmed to:

initialize the plurality of simulated wireless network environments, wherein each of the plurality of the simulated wireless network environments is defined by at least one simulated environment parameter, and wherein the at least one simulated environment parameter for each of the plurality of the simulated wireless network environments is determined by sampling from a plurality of probability distributions corresponding to each of the at least one simulated environment parameter;

add a plurality of simulated wireless nodes to each of the plurality of simulated wireless network environments to form a plurality of simulated wireless network environments; and

measure one or more simulated SINRs within each of the plurality of simulated wireless network environments.

13 . The system of claim 12 , wherein to train the SINR optimization machine learning algorithm based at least in part on the plurality of simulated wireless network environments, the one or more central computers are further programmed to:

determine one or more estimated SINRs for each of the plurality of simulated wireless network environments using the regression model based at least in part on the at least one simulated environment parameter of each of the plurality of simulated wireless network environments;

compare the one or more estimated SINRs to the one or more simulated SINRs for each of the plurality of simulated wireless network environments to determine a plurality of simulated SINR deviations; and

train the SINR optimization machine learning algorithm based at least in part on the at least one simulated environment parameter of each of the plurality of simulated wireless network environments and the plurality of simulated SINR deviations.

14 . A method for estimating signal to interference and noise ratio (SINR) in a wireless network environment, the method comprising:

receiving a plurality of wireless connection datasets from one or more wireless devices, wherein each of the plurality of wireless connection datasets includes at least one measured environment parameter and a measured SINR, and wherein the at least one measured environment parameter of each of the plurality of wireless connection datasets includes at least one of: a channel quality indication (CQI) value, a weather condition value, and an environment complexity value;

generating a regression model based at least in part on the plurality of wireless connection datasets, wherein the regression model is configured to determine an estimated SINR based on the at least one measured environment parameter; and

determining a plurality of tested SINR deviations between the estimated SINR determined by the regression model and the measured SINR in each of the plurality of wireless connection datasets; and

training an SINR optimization machine learning algorithm based at least in part on the plurality of tested SINR deviations, wherein the SINR optimization machine learning algorithm is trained to receive the at least one measured environment parameter and the estimated SINR as an input and provide an optimized estimated SINR as an output.

15 . The method of claim 14 , wherein training the SINR optimization machine learning algorithm further comprises:

generating a plurality of simulated wireless network environments using a computer simulation; and

training the SINR optimization machine learning algorithm to account for edge cases based at least in part on the plurality of simulated wireless network environments.

16 . The method of claim 15 , wherein training the SINR optimization machine learning algorithm based at least in part on the plurality of simulated wireless network environments further comprises:

determining one or more estimated SINRs for each of the plurality of simulated wireless network environments using the regression model based at least in part on at least one simulated environment parameter of each of the plurality of simulated wireless network environments;

comparing the one or more estimated SINRs to one or more simulated SINRs for each of the plurality of simulated wireless network environments to determine a plurality of simulated SINR deviations; and

training the SINR optimization machine learning algorithm based at least in part on the at least one simulated environment parameter of each of the plurality of simulated wireless network environments and the plurality of simulated SINR deviations.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (37)