Machine learning-assisted use case classification and adaptive antenna tuning

1. A method of generating an electronic device use case determination model for an electronic device, comprising:

generating a plurality of test measurements of a wireless data transmission system in a first type of electronic device, wherein each test measurement of the plurality of test measurements is associated with a predefined use case of the first type of electronic device; and

training a machine learning model based on the plurality of test measurements to determine a use case for the first type of electronic device based on a plurality of operational values of the first type of electronic device.

2. The method of claim 1 , wherein the machine learning model is a neural network model.

3. The method of claim 2 , wherein the plurality of test measurements comprise:

a real impedance of an element of the wireless data transmission system;

an imaginary impedance of the element of the wireless data transmission system;

a frequency of the wireless data transmission system;

and one or more of:

an impedance tuner state; or

an aperture tuner state.

4. The method of claim 3 , wherein the element is an antenna of the wireless data transmission system.

5. The method of claim 1 , further comprising:

generating a plurality of synthetic measurements based on the plurality of test measurements; and

training the machine learning model based on the plurality of synthetic measurements.

6. The method of claim 1 , wherein training the machine learning model further comprises:

training a first classifier of the machine learning model to determine whether the first type of electronic device is no longer in a first use case; and

training a second classifier of the machine learning model to determine a second use case for the first type of electronic device.

7. The method of claim 1 , further comprising:

determining a plurality of closed decision boundaries based on the trained machine learning model,

wherein each closed decision boundary of the plurality of closed decision boundaries is associated with a predefined use case of the first type of electronic device.

8. The method of claim 1 , further comprising: deploying the machine learning model to an electronic device of the first type.

9. A training system, comprising:

a memory comprising computer-executable instructions;

a processor configured to execute the computer-executable instructions and cause the training system to:

generate a plurality of test measurements of a wireless data transmission system in a first type of electronic device, wherein each test measurement of the plurality of test measurements is associated with a predefined use case of the first type of electronic device; and

train a machine learning model based on the plurality of test measurements to determine a use case for the first type of electronic device based on a plurality of operational values of the first type of electronic device,

wherein the machine learning model is a neural network model.

10. The training system of claim 9 , wherein the plurality of test measurements comprise:

a real impedance of an element of the wireless data transmission system;

an imaginary impedance of the element of the wireless data transmission system;

a frequency of the wireless data transmission system;

and one or more of:

an impedance tuner state; or

an aperture tuner state.

11. The training system of claim 10 , wherein the element is an antenna of the wireless data transmission system.

12. The training system of claim 9 , further comprising:

generating a plurality of synthetic measurements based on the plurality of test measurements; and

training the machine learning model based on the plurality of synthetic measurements.

13. The training system of claim 9 , wherein in order to train the machine learning model, the processor is further configured to cause the training system to:

train a first classifier of the machine learning model to determine whether the first type of electronic device is no longer in a first use case; and

train a second classifier of the machine learning model to determine a second use case for the first type of electronic device.

14. The training system of claim 9 , wherein:

the processor is further configured to cause the training system to determine a plurality of closed decision boundaries based on the trained machine learning model, and

each closed decision boundary of the plurality of closed decision boundaries is associated with a predefined use case of the first type of electronic device.

15. The training system of claim 9 , wherein the processor is further configured to cause the training system to: deploy the machine learning model to an electronic device of the first type.

16. A method for adaptively tuning a wireless data transmission system in an electronic device, comprising:

generating a plurality of measurements of an element of the wireless data transmission system in the electronic device;

determining a use case for the electronic device based on a use case determination model and the plurality of measurements;

determining one or more antenna settings associated with the determined use case;

tuning the wireless data transmission system based on the one or more antenna settings; and

transmitting data via the wireless data transmission system using the one or more antenna settings.

17. The method of claim 16 , wherein: the use case determination model is a neural network model.

18. The method of claim 17 , wherein the use case determination model is stored in a memory of a modem of the electronic device.

19. The method of claim 16 , wherein the plurality of measurements comprise:

a real impedance of the element of the wireless data transmission system;

an imaginary impedance of the element of the wireless data transmission system;

a frequency of the wireless data transmission system;

and one or more of:

an impedance tuner state; or

an aperture tuner state.

20. The method of claim 16 , wherein the use case determination model comprises:

a first classifier configured to determine whether the electronic device is no longer in a first use case; and

a second classifier configured to determine a second use case for the electronic device.

21. The method of claim 16 , wherein determining the use case for the electronic device comprises:

comparing the measurements to a plurality of closed decision boundaries,

wherein each closed decision boundary of the plurality of closed decision boundaries is associated with a predefined use case of the electronic device.

22. The method of claim 16 , wherein the one or more antenna settings associated with the determined use case comprise one or more of:

an impedance tuner setting; or

an aperture tuner setting.

23. The method of claim 16 , wherein determining the one or more antenna settings associated with the determined use case comprises:

querying a use case setting database; and

receiving the one or more antenna settings associated with the determined use case from the use case setting database.

24. The method of claim 16 , wherein tuning the wireless data transmission system based on the one or more antenna settings comprises: providing an aperture tuner setting to an aperture tuner of the electronic device.

25. The method of claim 16 , wherein tuning the wireless data transmission system based on the one or more antenna settings further comprises: providing an impedance tuner setting to an impedance tuner of the electronic device.

26. An electronic device, comprising:

a wireless data transmission system;

a memory comprising computer-executable instructions;

a processor configured to execute the computer-executable instructions and cause the electronic device to:

generate a plurality of measurements of an element of the wireless data transmission system in the electronic device;

determine a use case for the electronic device based on a use case determination model and the plurality of measurements;

determine one or more antenna settings associated with the determined use case;

tune the wireless data transmission system based on the one or more antenna settings; and

transmit data via the wireless data transmission system using the one or more antenna settings.

27. The electronic device of claim 26 , wherein: the use case determination model is a neural network model.

28. The electronic device of claim 27 , wherein;

the wireless data transmission system comprises a modem,

the modem comprises the memory, and

the use case determination model is stored in the memory of the modem.

29. The electronic device of claim 26 , wherein the plurality of measurements comprise:

a real impedance of the element of the wireless data transmission system;

an imaginary impedance of the element of the wireless data transmission system;

a frequency of the wireless data transmission system;

and one or more of:

an impedance tuner state; or

an aperture tuner state.

30. The electronic device of claim 26 , wherein the use case determination model comprises:

a first classifier configured to determine whether the electronic device is no longer in a first use case; and

a second classifier configured to determine a second use case for the electronic device.

31. The electronic device of claim 26 , wherein in order to determine the use case for the electronic device, the processor is further configured to cause the electronic device to:

compare the measurements to a plurality of closed decision boundaries,

wherein each closed decision boundary of the plurality of closed decision boundaries is associated with a predefined use case of the electronic device.

32. The electronic device of claim 26 , wherein the one or more antenna settings associated with the determined use case comprise one or more of:

an impedance tuner setting; or

an aperture tuner setting.

33. The electronic device of claim 26 , wherein in order to determine the one or more antenna settings associated with the determined use case, the processor is further configured to cause the electronic device to:

query a use case setting database; and

receive the one or more antenna settings associated with the determined use case from the use case setting database.

34. The electronic device of claim 26 , wherein in order to tune the wireless data transmission system based on the one or more antenna settings, the processor is further configured to cause the electronic device to: provide an aperture tuner setting to an aperture tuner of the electronic device.

35. The electronic device of claim 26 , wherein in order to tune the wireless data transmission system based on the one or more antenna settings, the processor is further configured to cause the electronic device to: provide an impedance tuner setting to an impedance tuner of the electronic device.