A1 POLICY FUNCTIONS FOR OPEN RADIO ACCESS NETWORK (O-RAN) SYSTEMS

    公开(公告)号:US20240214272A1

    公开(公告)日:2024-06-27

    申请号:US18550276

    申请日:2022-07-05

    申请人: Intel Corporation

    IPC分类号: H04L41/0894

    CPC分类号: H04L41/0894

    摘要: The present invention relates to an apparatus comprising: memory to store policy statement information for a plurality of radio access network (RAN) automation applications (rApps); and processing circuitry, coupled with the memory, to: retrieve the policy statement information from the memory, wherein the policy statement information includes respective policy scope identifiers for respective rApps in the plurality of rApps; identify a conflict associated with common or overlapping policy scope identifiers between two or more rApps from the plurality of rApps; modify one or more A1 policies associated with an A1 interface connecting a non-real-time (non-RT) RAN intelligence controller (RIC) and a near-real-time (near-RT) RIC to resolve the conflict; and notify the two or more rApps of the modification of the one or more A1 policies.

    SECURED HD MAP SERVICES USING BLOCKCHAIN

    公开(公告)号:US20220286305A1

    公开(公告)日:2022-09-08

    申请号:US17638728

    申请日:2019-09-27

    申请人: Intel Corporation

    摘要: Various systems and methods for implementing secure high-definition map services are described herein. A system for implementing secure high-definition map services includes a processor subsystem; and a storage including instructions, which when executed by the processor subsystem, cause the processor subsystem to: receive map data from a remote data source; authenticate the remote data source; obtain an identifier of the remote data source; add the map data to an entry in an immutable log when the remote data source is authenticated, the entry having an entry identifier; store an association between the identifier of the remote data source and the entry identifier in a secure store; and incorporate the map data into a master map.

    DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE

    公开(公告)号:US20240137288A1

    公开(公告)日:2024-04-25

    申请号:US18399314

    申请日:2023-12-28

    申请人: Intel Corporation

    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.

    Data-centric service-based network architecture

    公开(公告)号:US11902104B2

    公开(公告)日:2024-02-13

    申请号:US17186526

    申请日:2021-02-26

    申请人: Intel Corporation

    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.