Selective triggering of neural network functions for positioning of a user equipment

1. A method of operating a user equipment (UE), comprising:

obtaining, by the UE, information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures;

receiving, from a network component, a query for current information associated with the UE;

transmitting, to the network component in response to the query, the obtained information;

receiving, from the network component in response to the transmission of the obtained information, an indication of at least one neural network function based on the obtained information satisfying the at least one triggering criterion;

obtaining, by the UE, positioning measurement data associated with a location of the UE; and

determining, by the UE, a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

2. The method of claim 1 , wherein the set of triggering criteria is received at the UE from a serving network or an external server.

3. The method of claim 1 , wherein the indication comprises the at least one neural network function or a reference to the at least one neural network function.

4. The method of claim 1 , wherein the query is received at the UE responsive to a handoff of the UE, or

wherein the indication is received at the UE responsive to the handoff of the UE, or

a combination thereof.

5. The method of claim 1 , wherein the set of neural network functions is aggregated into a single neural network function construct.

6. The method of claim 5 ,

wherein the obtained information is provided as a set of inputs into the single neural network function construct, and

wherein the determining comprises execution of the single neural network function construct based on the set of inputs.

7. The method of claim 1 , wherein the at least one neural network function comprises a UE-feature processing neural network function, at least one base station (BS)-feature processing neural network function, or a combination thereof.

8. The method of claim 1 , wherein the obtained information comprises one or more of geographic region characteristics of the UE, whether the UE is located in an indoor or outdoor environment, a serving base station or carrier network of the UE, a UE category, a base station category, or any combination thereof.

9. The method of claim 1 , wherein the set of triggering criteria is associated with one or more of geographic region characteristics, an indoor or outdoor UE status, a base station or carrier network, a UE category, a base station category, or any combination thereof.

10. A user equipment (UE), comprising:

a memory;

at least one transceiver; and

at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to:

obtain information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures;

receive, from a network component, a query for current information associated with the UE;

transmit, to the network component in response to the query, the obtained information;

receive, from the network component in response to the transmission of the obtained information, an indication of at least one neural network function based on the obtained information satisfying the at least one triggering criterion;

obtain positioning measurement data associated with a location of the UE; and

determine a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

11. The UE of claim 10 , wherein the set of triggering criteria is received at the UE from a serving network or an external server.

12. The UE of claim 10 , wherein the indication comprises the at least one neural network function or a reference to the at least one neural network function.

13. The UE of claim 10 ,

wherein the query is received at the UE responsive to a handoff of the UE, or

wherein the indication is received at the UE responsive to the handoff of the UE, or

a combination thereof.

14. The UE of claim 10 , wherein the set of neural network functions is aggregated into a single neural network function construct.

15. The UE of claim 14 ,

wherein the obtained information is provided as a set of inputs into the single neural network function construct, and

wherein the determining comprises execution of the single neural network function construct based on the set of inputs.

16. The UE of claim 10 , wherein the at least one neural network function comprises a UE-feature processing neural network function, at least one base station (BS)-feature processing neural network function, or a combination thereof.

17. The UE of claim 10 , wherein the obtained information comprises one or more of geographic region characteristics of the UE, whether the UE is located in an indoor or outdoor environment, a serving base station or carrier network of the UE, a UE category, a base station category, or any combination thereof.

18. The UE of claim 10 , wherein the set of triggering criteria is associated with one or more of geographic region characteristics, an indoor or outdoor UE status, a base station or carrier network, a UE category, a base station category, or any combination thereof.

19. A user equipment (UE), comprising:

means for obtaining information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures;

means for receiving, from a network component, a query for current information associated with the UE;

means for transmitting, to the network component in response to the query, the obtained information;

means for receiving, from the network component in response to the transmission of the obtained information, an indication of at least one neural network function based on the obtained information satisfying the at least one triggering criterion;

means for obtaining positioning measurement data associated with a location of the UE; and

means for determining a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

20. The UE of claim 19 , wherein the set of triggering criteria is received at the UE from a serving network or an external server.

21. The UE of claim 19 , wherein the indication comprises the at least one neural network function or a reference to the at least one neural network function.

22. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE), cause the UE to:

obtain information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures;

receive, from a network component, a query for current information associated with the UE:

transmit, to the network component in response to the query, the obtained information;

receive, from the network component in response to the transmission of the obtained information, an indication of at least one neural network function based on the obtained information satisfying the at least one triggering criterion;

obtain positioning measurement data associated with a location of the UE; and

determine a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria,

wherein the UE is a subscriber device that subscribes to one or more communications services provided via a communications network.

23. The non-transitory computer-readable medium of claim 22 ,

wherein the query is received at the UE responsive to a handoff of the UE, or

wherein the indication is received at the UE responsive to the handoff of the UE, or

a combination thereof.