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Method for end-to-end (E2E) user equipment (UE) trajectory network automation based on future UE location

專利號
US10785634B1
公開日期
2020-09-22
申請人
Telefonaktiebolaget LM Ericsson (publ)(SE Stockholm)
發(fā)明人
Virgilio Fiorese; Saulo Almeida Montenegro de Sa; Rakesh Bajpai; Vinicius Samuel Landi Fiorese; Tushar Sabharwal; Nipun Sharma; Rohit Shukla
IPC分類
H04W8/08; G06N5/04; H04W64/00
技術(shù)領(lǐng)域
ue,network,e2e,nwdaf,trajectory,mobility,in,or,node,plmn
地域: Stockholm

摘要

Methods and systems for End-to-End (E2E) User Equipment (UE) trajectory network automation are herein provided. According to one aspect, a network node for E2E UE trajectory network automation, such as a Network Data Analytics Function (NWDAF), receives, from a requesting entity, information identifying a future E2E UE trajectory, the E2E UE trajectory comprising a start location, an end location, and zero or more intermediate locations between the start location and the end location; calculates a E2E mobility trajectory prediction for the identified future E2E UE trajectory; and sends, to the requesting entity, the calculated E2E mobility trajectory prediction. The requesting entity may be a trusted entity or an untrusted entity, such as a Third Party Provider (3PP) outside of the trusted domain of the network. If the requesting entity selects a mobility trajectory, the network node sends mobility management and optimization information to a Radio Access Network node.

說明書

According to one aspect, the present disclosure provides a set of APIs to be implemented by core network nodes, such as SCEF/Network Exposure Function (NEF), User Plane Function (UPF), etc., which will allow Geographic Information System (GIS) based applications to send tentative E2E trajectory information to the network analytics and automation functions, such as NWDAF, PCF, etc. In some embodiments, the network analytics and automation functions will parse the information, adding future UE location timestamps, and process it against the layered GIS information that supports different network perspectives such as performance, capacities, availability, etc. After the request is processed, the network analytics and automation will send back to the GIS based apps the expected network performance of the proposed mobility trajectory and alternative trajectories with better or similar network performance. GIS based apps will select the preferred mobility trajectory based on network performance and estimated time of arrival, and after the selection the network analytics and automation functions will adjust the network properly to deliver the predicted network performance.

權(quán)利要求

1
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