Enhanced AI/ML Based Mobility Optimization

An apparatus configured for wireless communications at a first target network node, comprising:

• one or more memories;
• and
• one or more processors configured to cause the apparatus to:
• receive a handover request associated with handing over a user equipment (UE) to a cell associated with the apparatus, the handover request comprising an identifier (ID) of a source network node and predicted information associated with the UE, the predicted information generated by a machine learning (ML) model;
• and
• transmit, to the source network node, an information update message comprising:
• feedback information comprising feedback on the predicted information, the feedback information associated with refining the ML, and
• a first persistent ID assigned to the UE, wherein the first persistent ID remains invariant across different radio resource control (RRC) modes of the UE, wherein the different RRC modes comprise an idle mode, an inactive mode, and a connected mode.

An apparatus configured for wireless communications at a user equipment (UE), comprising:

• one or more memories;
• and
• one or more processors configured to cause the apparatus to:
• receive, from a first target network node communicating with the apparatus, an RRC release message indicating that the apparatus is to transition from a connected mode to an idle mode or an inactive mode, the RRC release message comprising an identifier (ID) of a source network node configured to generate predicted information for the apparatus via a machine learning (ML) model;
• transition from the connected mode to the inactive mode or the idle mode in response to receiving the RRC release message;
• and
• transmit, to a second target network node, an RRC connection request requesting to re-establish or resume a RRC connection with the second target network node, the RRC connection request comprising the ID of the source network node.

A method for wireless communications by an apparatus at a source network node, comprising:

• transmitting a handover request associated with handing over a user equipment (UE) from a first cell associated with the apparatus to a second cell associated with a first target network node, the handover request comprising an identifier (ID) of the apparatus and predicted information associated with the UE, the predicted information generated by a machine learning (ML) model;
• and
• receiving an information update message comprising:
• feedback information comprising feedback on the predicted information, the feedback information associated with refining the ML model, and
• a first persistent ID assigned to the UE, wherein the first persistent ID remains invariant across different radio resource control (RRC) modes of the UE, wherein the different RRC modes comprise an idle mode, an inactive mode, and a connected mode.