Neural network functions for positioning of a user equipment

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 at least one neural network function configured to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures;
• 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.

A base station (BS), 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 at least one neural network function configured to facilitate a user equipment (UE) to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures;
• and
• transmit, via the at least one transceiver, the at least one neural network function to the UE.