User equipment trajectory-assisted handover
Systems, apparatuses, methods, and computer-readable media are provided for user equipment (UE) trajectory-assisted handovers. In particular, some embodiments may include artificial intelligence (AI) or machine learning (ML) to predict UE location information. Other embodiments may be described and/or claimed.
1 . An apparatus comprising:
memory to store user equipment (UE) measurement report information; and
processing circuitry, coupled with the memory, to:
decode a request for location information for the UE received from a network element;
retrieve the UE measurement report information from the memory;
determine, using a positioning artificial intelligence (AI) or machine learning (ML) model, the location information for the UE based on the UE measurement report information and the request for location information, wherein the location information for the UE includes an indication of a predicted location for the UE; and
encode a message, for transmission to the network element, that includes an indication of the location information for the UE,
wherein the location information for the UE includes an indication of at least one of a current or predicted resource status for at least one of a current cell of the UE and a target cell of the UE.
2 . The apparatus of claim 1 , wherein the UE measurement report information includes historical and current information for the UE.
3 . The apparatus of claim 2 , wherein the historical and current information for the UE includes:
location information, speed information, or trajectory information.
4 . The apparatus of claim 1 , wherein the location information for the UE includes an indication of a predicted load of the UE.
5 . The apparatus of claim 1 , wherein the location information for the UE includes the indication of the current or predicted resource status for the current cell of the UE.
6 . The apparatus of claim 1 , wherein the location information for the UE includes the indication of the current or predicted resource status for the target cell of the UE.
7 . The apparatus of claim 1 , wherein:
the UE measurement report information includes an indication of a current or predicted speed of the UE and an indication of a current or predicted direction of motion of the UE, and
the positioning AI or ML model determines the location information for the UE based on the indication of the current or predicted speed of the UE and the indication of the current or predicted direction of motion of the UE.
8 . The apparatus of claim 1 , wherein the location information for the UE includes a plurality of predicted locations for the UE corresponding to a plurality of future time points within a requested duration.
9 . The apparatus of claim 1 , wherein the message encoded for transmission to the network element further includes an indication of an accuracy of the determined location information for the UE.
10 . The apparatus of claim 1 , wherein:
the processing circuitry is further to update the positioning AI or ML model based on a performance indicator received from a 5 th generation NodeB (gNB), and
the performance indicator indicates a handover outcome for the UE, the handover outcome being successful or unsuccessful.
11 . The apparatus of claim 1 , wherein:
the request for location information includes a requested duration for the predicted location information, and
the requested duration is defined by a start timestamp and an end timestamp or by a start timestamp and a duration parameter.
12 . The apparatus of claim 1 , wherein the processing circuitry is further configured to select the UE for AI or ML model-based location determination based on at least one of: a number of failed handover executions associated with the UE exceeding a threshold, a speed of the UE being below a threshold speed, or a device type of the UE indicating that the UE operates in an environment in which a trajectory of the UE is predictable.
13 . The apparatus of claim 1 , wherein:
the processing circuitry is further configured to store in memory the UE measurement report information over a plurality of measurement intervals to build a historical record for the UE, and
the positioning AI or ML model is trained based on the historical record.
14 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause a network function to:
decode a request for location information for a user equipment (UE) received from a network element;
determine, using a positioning artificial intelligence (AI) or machine learning (ML) model, the location information for the UE based on UE measurement report information and the request for location information, wherein the location information for the UE includes an indication of a predicted location for the UE; and
encode a message, for transmission to the network element, that includes an indication of the location information for the UE,
wherein the location information for the UE includes an indication of at least one of a current or predicted resource status for at least one of a current cell of the UE and a target cell of the UE.
15 . The one or more non-transitory computer-readable media of claim 14 , wherein the UE measurement report information includes historical and current information for the UE.
16 . The one or more non-transitory computer-readable media of claim 15 , wherein the historical and current information for the UE includes:
location information, speed information, or trajectory information.
17 . The one or more non-transitory computer-readable media of claim 14 , wherein the location information for the UE includes an indication of a predicted load of the UE.
18 . The one or more non-transitory computer-readable media of claim 14 , wherein the location information for the UE includes the indication of the current or predicted resource status for the current cell of the UE.
19 . The one or more non-transitory computer-readable media of claim 14 , wherein the location information for the UE includes the indication of the current or predicted resource status for the target cell of the UE.
20 . The one or more non-transitory computer-readable media of claim 14 , wherein the network function includes:
a network data analytics function (NWDAF), operation administration and maintenance (OAM) function, or location management function (LMF).
21 . The apparatus of claim 1 , wherein the processing circuitry is to implement a network data analytics function (NWDAF), operation administration and maintenance (OAM) function, or location management function (LMF).