Intel Corporation

Leifeng Ruan

Qian Li

Dawei Ying

Geng Wu

Zongrui Ding

Xiaopeng Tong

Jaemin Han

Dario Sabella

Soo Jin Tan

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.

A1 policy functions for open radio access network (O-RAN) systems

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 AI-ML producer and an AI-ML consumer, and terminating the use of an ML mode. The ML model monitoring includes the AI-ML consumer sending monitoring data to the AI-ML producer and the AI-ML producer processing the monitoring data and taking actions based on the monitoring data. The services may be performed over the AI interface using HTTP.

ML model management in O-RAN

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

Systems, apparatuses, methods, and computer-readable media, are provided for selecting edge or central servers for serving client systems based on network events monitored by one or more network elements. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

Server selection for vehicle communications and applications

Systems, apparatuses, methods, and computer-readable media, are provided for managing background data transfer sessions based on network events monitored by one or more network elements. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

Vehicle-to-everything session and service continuity in automotive edge computing systems

Systems, apparatuses, methods, and computer-readable media, are provided for selecting edge or central servers for serving client systems based on network events monitored by one or more network elements. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consorium (AECC) technologies. Other embodiments may be described and/or claimed.

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Beijing, China

Leifeng Ruan