Abstract: Various systems and methods for implementing a multi-access edge computing (MEC) based system to realize 5G Network Edge and Core Service Dimensioning using Machine Learning and other Artificial Intelligence Techniques, for improved operations and usage of computing and networking resources, and are disclosed herein. In an example, processing circuitry of a compute node on a network is used to analyze execution of an application to obtain operational data. The compute node then may modularize functions of the application based on the operational data to construct modularized functions. A phase transition graph is constructed using a machine-learning based analysis, the phase transition graph representing state transitions from one modularized function to another modularized function, where the phase transition graph is used to dimension the application by distributing the modularized functions across the network.

Abstract: Various approaches for the deployment and coordination of network operation processing, compute processing, and communications for 5G networks, including with the use of fingerprint-based vRAN cell integrity monitoring, are discussed. In an example, analyzing a state of a 5G network includes: obtaining initial fingerprint reference data of a network state between a virtualized radio access network (vRAN) node and at least one fingerprint reference unit (FRU) device wirelessly connected to the vRAN node; comparing the initial fingerprint reference data to subsequent fingerprint data of the network state between the vRAN node (e.g., operating as vRAN gNB, or as an IAB Donor or IAB Node) and the at least one FRU device to detect a changed network condition; and performing an action at the vRAN node to modify or disable a component of the 5G network, in response to detection of the changed network condition.

Abstract: Various approaches for detecting and mitigating interference in a supplemental coverage from space (SCS) networking arrangement are disclosed, using an SCS zone for a geographic area in connection with exclusion, coordination, or inclusion of SCS communications in the geographic area. In an example, an approach for dynamically mitigating interference includes: obtaining orbital position data for at least one satellite vehicle (e.g., low-earth orbit (LEO) SV), which can perform SCS network communications to a geographic area that includes a terrestrial network (e.g., 5G network); determining operational parameters to mitigate terrestrial interference of the SCS network communications in the geographic area, with the geographic area identified based on the orbital position data; and modifying operation of the SCS network communications in the geographic area, based on the determined operational parameters.

Abstract: Various systems and methods for implementing end-to-end quality of service (QoS) for network communications are provided using various network and compute technologies. In an example, managing Quality of Service (QoS) for end-to-end network data flows, includes: identifying QoS characteristics for data flows of a user equipment (UE), for data flows performed via multiple access networks; mapping the QoS characteristics to network functions of at least one of the multiple access networks; and controlling the network functions of the at least one of the multiple access networks, based on the QoS characteristics, as the network functions are implemented at respective resources located within at least one of the multiple access networks. Further examples for controlling the network functions using Access Traffic Steering, Switching and Splitting (ATSSS) functionality in an 3GPP multi-access network, and configuring a network exposure function of an 3GPP multi-access network, are also disclosed.

Abstract: Various approaches for the deployment and coordination of terrestrial cellular (e.g., 5G) network exclusion or mitigation zone, for the dynamic deployment of radio frequency blocking for in-motion aerial devices are described. Approaches are also described for a space-based non-terrestrial network exclusion or mitigation zone, including for maintenance or multi-constellation/multi-orbit coordination. The calculation, distribution, deployment and use of exclusion and inclusion zones are described for coordination with such a terrestrial cellular network or non-terrestrial satellite network.

Abstract: Various systems and methods for implementing an edge computing system to realize 5G network slices with blockchain traceability for informed 5G service supply chain are disclosed. A system configured to track network slicing operations includes memory and processing circuitry configured to select a network slice instance (NSI) from a plurality of available NSIs based on an NSI type specified by a client node. The available NSIs uses virtualized network resources of a first network resource provider. The client node is associated with the selected NSI. The utilization of the network resources by the plurality of available NSIs is determined using an artificial intelligence (AI)-based network inferencing function. A ledger entry of associating the selected NSI with the client node is recorded in a distributed ledger, which further includes a second ledger entry indicating allocations of resource subsets to each of the NSIs based on the utilization.

Abstract: Various approaches for the deployment and coordination of network operation processing, compute processing, and inter-satellite communication coordination, within one or multiple satellite non-terrestrial networks, are discussed. Among other examples, a data center located at one or more satellites operating in a middle Earth orbit (MEO) plane, geosynchronous orbit (GEO) plane, or high-Earth elliptical orbit (HEO) plane, may be used to provide network and data processing operations for a low-Earth orbit (LEO) constellation.

Abstract: Various approaches for the deployment and use of communication exclusion zones, defined for use with a satellite non-terrestrial network (including within a low-earth orbit satellite constellation), are discussed. In an example, defining and implementing a non-terrestrial communication exclusion zone includes: calculating based on a future orbital position of a low-earth orbit satellite vehicle, an exclusion condition for communications from the satellite vehicle; identifying, based on the exclusion condition and the future orbital position, a timing for implementing the exclusion condition for the communications from the satellite vehicle; and generating exclusion zone data for use by the satellite vehicle, the exclusion zone data indicating the timing for implementing the exclusion condition for the communications from the satellite vehicle.

Abstract: Various systems and methods for implementing a multi-access edge computing (MEC) based system to realize 5G Network Edge and Core Service Dimensioning using Machine Learning and other Artificial Intelligence Techniques, for improved operations and usage of computing and networking resources, and are disclosed herein. In an example, processing circuitry of a compute node on a network is used to analyze execution of an application to obtain operational data. The compute node then may modularize functions of the application based on the operational data to construct modularized functions. A phase transition graph is constructed using a machine-learning based analysis, the phase transition graph representing state transitions from one modularized function to another modularized function, where the phase transition graph is used to dimension the application by distributing the modularized functions across the network.

Abstract: Various approaches for the deployment and coordination of inter-satellite communication pathways, defined for use with a satellite non-terrestrial network, are discussed. Among other examples, such inter-satellite communication pathways may be identified, reserved, allocated, and used for ultra-low-latency communication purposes.

Abstract: Various systems and methods for implementing a multi-access edge computing (MEC) based system to realize 5G Network Edge and Core Service Dimensioning using Machine Learning and other Artificial Intelligence Techniques, for improved operations and usage of computing and networking resources, and are disclosed herein. In an example, processing circuitry of a compute node on a network is used to analyze execution of an application to obtain operational data. The compute node then may modularize functions of the application based on the operational data to construct modularized functions. A phase transition graph is constructed using a machine-learning based analysis, the phase transition graph representing state transitions from one modularized function to another modularized function, where the phase transition graph is used to dimension the application by distributing the modularized functions across the network.

Abstract: An apparatus includes a processor to receive a plurality of telemetry datasets from a plurality of infrastructure processing units (IPUs) in a computing infrastructure. Each of the plurality of IPUs is operably coupled to a plurality of devices having a particular device type. The plurality of telemetry datasets includes a first telemetry dataset received from a first IPU and a second telemetry dataset received from a second IPU. The processor is to store first telemetry data from the first telemetry dataset in a data store, store second telemetry data from the second telemetry dataset in the data store, and in response to receiving a telemetry data request that specifies a first identifier identifying the first IPU and a job identifier, retrieve the first telemetry data from the data store based, at least in part, on the first telemetry data being associated with the first identifier and the job identifier.

Abstract: Various systems and methods for implementing a multi-access edge computing (MEC) based system to realize 5G Network Edge and Core Service Dimensioning using Machine Learning and other Artificial Intelligence Techniques, for improved operations and usage of computing and networking resources, and are disclosed herein. In an example, processing circuitry of a compute node on a network is used to analyze execution of an application to obtain operational data. The compute node then may modularize functions of the application based on the operational data to construct modularized functions. A phase transition graph is constructed using a machine-learning based analysis, the phase transition graph representing state transitions from one modularized function to another modularized function, where the phase transition graph is used to dimension the application by distributing the modularized functions across the network.

Abstract: A system and method for vehicle communication, including a computing device communicatively coupled with a roadside computing device disposed along a road, wherein the computing device is disposed remote from the road. The roadside computing device to communicate with a vehicle computing system of a vehicle on the road.

Abstract: Methods, systems, and use cases for extended P2P communication with edge networking are discussed, including an edge computing device with a memory device and processing circuitry. The processing circuitry receives a request from a second edge computing device to perform a P2P exchange. A set of services for execution by the processing circuitry during the P2P exchange is determined. The processing circuitry further determines whether an enhanced edge service is available to substitute at least one service of the set of services. The enhanced edge service is associated with processing resources that are external to the edge computing device. Based on a successful determination that the enhanced edge service is available, the processing resources of the edge computing system that are external to the edge computing device are utilized to execute the enhanced edge service in place of the at least one service during the P2P exchange.

Abstract: A system and method for vehicle communication, including a computing device communicatively coupled with a roadside computing device disposed along a road, wherein the computing device is disposed remote from the road. The roadside computing device to communicate with a vehicle computing system of a vehicle on the road.

Abstract: Various systems and methods for implementing an edge computing system to realize 5G network slices with blockchain traceability for informed 5G service supply chain are disclosed. A system configured to track network slicing operations includes memory and processing circuitry configured to select a network slice instance (NSI) from a plurality of available NSIs based on an NSI type specified by a client node. The available NSIs uses virtualized network resources of a first network resource provider. The client node is associated with the selected NSI. The utilization of the network resources by the plurality of available NSIs is determined using an artificial intelligence (AI)-based network inferencing function. A ledger entry of associating the selected NSI with the client node is recorded in a distributed ledger, which further includes a second ledger entry indicating allocations of resource subsets to each of the NSIs based on the utilization.