Method for end-to-end (E2E) user equipment (UE) trajectory network automation based on future UE location
摘要
說明書
The fourth excerpt from 3GPP TS 23.791 identifies autonomous driving as something that would benefit from the kinds of analysis that could be performed by an NWDAF, and identifies some issues that should be considered by the working group.
In short, 3GPP TS 23.791 identifies a number of areas that are subjects for future study, but does not propose any solutions to those problems. Although the NWDAF can provide valuable information about, and insight into, a network's current condition, conventional NWDAFs have access only to data provided to it by other nodes within the core network, such as a 5G Core Network (5GC). While this allows the NWDAF to make predictions about a UE's trajectory, based on extrapolation of historical location information, for example, such predictions have limited confidence—past behavior is no guarantee of future behavior. Because a conventional NWDAF can make only rough and uncertain predictions about mobility trajectories, the ability of network automation to proactively address or even avoid problems such as network capacity overload due to mobile device trajectories, for example, is severely limited. This is in large part due to the fact the conventional core networks do not have advance information about where a mobile user is planning to go or when the mobile user is planning to go there. At best, network automation can only make predictions about where a next handover is likely to occur, e.g., it may be able to predict a next hop. In short, one weakness of conventional telecommunication network automation is that the network itself has no way to determine an “End-to-End” (E2E) trajectory of a mobile device.
