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

專(zhuān)利號(hào)
US10785634B1
公開(kāi)日期
2020-09-22
申請(qǐng)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(lèi)
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(shuō)明書(shū)

The third excerpt from 3GPP TS 23.791 investigates how the NWDAF might use Quality of Service (QoS) information received from different network functions and application functions within the Public Land Mobile Network (PLMN) domain to identify segments or areas of the network where QoS could be improved.

Begin Excerpt 4 from 3GPP TS 23.791

    • 5.1.12 Use Case 12: <NWDA-Assisted predictable network performance>
    • 5.1.12.1 Description
    • During autonomous driving, it would be helpful for advancing vehicles case to get predictable network performance (e.g. latency, reliability) of upcoming NG-RAN, i.e. eV2X application server can decide whether keeping autonomous driving mode in the upcoming NG-RAN based on the predicted network performance. Network performance of upcoming NG-RAN analyzed/predicted by NWDAF may consider the factors, e.g. speed and direction or upcoming location of the vehicle, network performance related information (load information based on time and spatial information).
    • In order to assist the decision of eV2X application server bases on predictable network performance from NWDA output, this use case considers the following issues:
      • What analytical result is required to be provided by the NWDA to V2X application server?
      • What input information is required for NWDAF to derive the analytical result and how to get this input information?

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