Abstract: 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.

Abstract: 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.

Abstract: Various embodiments herein provide techniques related to communication between a non real-time (Non-RT) radio access network (RAN) intelligent controller (RIC) and a near real-time (Near-RT) RIC. Specifically, the Non-RT RIC may identify physical positioning information related to a user equipment (UE); generate enrichment information (EI) related to the physical positioning information; and transmit the EI over an A1 interface to the Near-RT RIC. Other embodiments may be described and/or claimed.

Abstract: This invention related to an apparatus comprising memory to store updated radio access network (RAN) user equipment (UE) identification (ID) information, and processing circuitry, coupled with the memory, to: receive, from a near-real time RAN intelligent controller (near-RT RIC), a subscription or request for the updated RAN UE ID information; retrieve the updated RAN UE ID information from the memory; and encode a message for transmission to the near-RT RIC that includes the updated RAN UE ID information.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems for providing SON functions running on an O-RAN RIC are described. To provide resource allocation for a non-real time RIC, the SON function monitors at least one RAN through collection of data from a network function. The data is analyzed to determine whether a network issue is present and, if so, is to be resolved. The SON function determines a SON action to be executed to resolve the network issue, and subsequently executes the SON action. Performance measurements of the network function are collected after execution of the SON action. The performance measurements are analyzed to evaluate whether the SON action has resolved the network issues and, if the network issues are not resolved, further SON actions are determined and subsequently executed.

Abstract: 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.

Abstract: 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.

Abstract: 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.