ML MODEL MANAGEMENT IN O-RAN
    6.
    发明申请

    公开(公告)号:US20220014942A1

    公开(公告)日:2022-01-13

    申请号:US17484074

    申请日:2021-09-24

    摘要: Apparatuses for non real-time (Non-RT) radio access network intelligence controller (RIC) and Near-RT RIC services for machine learning (ML) model management in an open radio access network (O-RAN) are disclosed. The services include ML model monitoring, getting and putting ML models from and to an A1-ML producer and an A1-ML consumer, and terminating the use of an ML mode. The ML model monitoring includes the A1-ML consumer sending monitoring data to the A1-ML producer and the A1-ML producer processing the monitoring data and taking actions based on the monitoring data. The services may be performed over the A1 interface using HTTP.

    FEDERATED LEARNING IN O-RAN
    7.
    发明申请

    公开(公告)号:US20220012645A1

    公开(公告)日:2022-01-13

    申请号:US17483590

    申请日:2021-09-23

    IPC分类号: G06N20/20 H04L29/08 H04W24/02

    摘要: Apparatuses for non real-time (Non-RT) radio access network intelligence controller (RIC) services for machine learning (ML) in an open radio access network (O-RAN) and apparatuses for Near-RT RIC services are disclosed. The services include ML capability query, federated learning session creation, federated learning session deletion, global model download/update, local model upload/update, global model status query, local model status query, global model status notification, and local model status notification. The services may be performed over the A1 interface using HTTP.

    DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE

    公开(公告)号:US20210184989A1

    公开(公告)日:2021-06-17

    申请号:US17186526

    申请日:2021-02-26

    摘要: A data-centric network and non-Real-Time (RT) RAN Intelligence Controller (RIC) architecture are described. The data-centric network architecture provides data plane functions (DPFs) that serve as a shared database for control functions, user functions and management functions for data plane resources in a network. The DPFs interact with control plane functions, user plane functions, management plane functions, compute plane functions, network exposure functions, and application functions of the NR network via a service interface. The non-RT RIC provides functions via rApps, manages the rApps, performs conflict mitigation and security functions, monitors machine learning (ML) performance, provides a ML model catalog that contains ML model information, provides interface terminations and stores ML data and Near-RT RIC related information in a database. An ML training host trains and evaluates ML models in the catalog, obtains training and testing data from the database, and retrains and updates the ML models.