Abstract: Methods and systems for dynamically re-routing layer traffic between different servers with little user-visible disruption and without modifications to the vRAN software stack are provided. This approach enables operators to initiate a PHY migration either on demand (e.g., during planned maintenances) or to set up automatic migration on unexpected events (e.g., server failures). It is recognized that PHY processing in cellular networks has no hard state that must be migrated. As a result, layer traffic such as the PHY-L2 traffic or L2-PHY traffic can be simply re-routed to a different server. This re-routing mechanism is realized by interposing one or more message controllers (e.g., middlebox) in a communication channel between the PHY and L2.

Abstract: The present optimization of guard bands repurposes some guard band spectrum for data transmission in a 5G network. This approach takes spectrum that is otherwise “wasted” for guard bands to enable overall spectrum utilization to be increased. To mitigate effects of inter-numerology interference (INI) with narrower guard band bandwidth, physical resource blocks (PRBs) for particular user equipment (UE) are allocated to BWPs that are modified with increased bandwidth that comes from narrowing the guard band bandwidth. These particular UEs have high signal strength, for example, as characterized by SINR (signal-to-interference plus noise ratio), relative to other UE. Allocating PRBs for high signal strength UE in BWPs near the edges of the narrower guard band increases the risk of INI, but the higher signal strength for these UE helps to lessen the INI impact and enable overall throughput for all users to be maximized in the network.

Abstract: A context analysis system receives a query from a user. The context analysis system generates one or multiple context profiles and generates a prompt for a foundation model for each of the context profiles. The context analysis system analyzes each of the context profiles and generates a relevancy score. The context analysis system selects one of the context profiles based on the relevancy score. In some examples, the context analysis system iteratively determines predicted latencies and relevancies of processing a query in conjunction with a generated context and, based on the predicted latencies and/or relevancies, processes the query using a foundation model, such as a large language model (LLM).

Abstract: A method for improving efficiency of routing edge compute traffic from a user equipment (UE) to an edge compute server at a far edge of a cellular network includes provisioning a near edge control unit (CU) and a near edge user plane function (UPF) at a near edge of the cellular network. The method also includes provisioning a far edge CU, a far edge UPF, and an edge compute workload at the far edge. The method also includes receiving UE traffic at one or more distributed units located at the far edge. The UE traffic includes the edge compute traffic and non-edge compute traffic. The method also includes identifying the edge compute traffic among the UE traffic, routing the edge compute traffic to the edge compute workload at the far edge, and routing the non-edge compute traffic to the near edge UPF at the near edge.

Abstract: Described are examples for providing a distributed fault-tolerant state store for a virtualized base station. In an aspect, a first server at a datacenter may perform physical layer processing for at least one virtualized base station. While performing the physical layer processing, the first server may generate inter-slot physical layer state data during a first slot. The inter-slot physical layer state data is to be used in a subsequent slot. The first server may periodically transmit the inter-slot physical layer state data to one or more other servers of the plurality of servers within the datacenter. One of the other servers may take over the physical layer processing for the at least one virtualized base station based on the inter-slot physical layer state data, for example, in response to a fault at the first server or a migration of the at least one virtualized base station.

Abstract: Aspects include a machine learning based resource block scheduler configured to meet service level requirements of applications. Aspects include receiving a plurality of scheduling requests each associated with a respective application of a plurality of applications on a plurality of wireless devices, identifying a plurality of current channel state information each associated with one of the plurality of wireless devices, and identifying a plurality of different types of service level requirements each associated with one of the plurality of applications.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: Slices of a 5G network may be configured to implement a trust model by which network customers are provided with assurances that slice properties meet agreed-upon criteria specified by customer policy so that slices can be trusted. Illustrative slice properties may pertain to service types, geographic area of operations, and attributes associated with software, firmware, and hardware used in the infrastructure of nodes in a trusted slice. Particular values of the properties describe a slice configuration that may be measured, digested, and attested to the customer to provide assurances that the configuration conforms with the policy. The 5G slice trust model may be implemented as a two-way model in which a slice provider performs checks to verify slice properties while customers ensure that only authenticated and authorized user equipment (UE) will access a trusted slice.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: The present application relates to egressing traffic from a public cloud network. An egress traffic manager configures routing at hosts and edge routers within the public cloud network. The egress traffic manager determines, for an edge router, a plurality of current border gateway protocol (BGP) sessions with external networks. The egress traffic manager configures a virtual router hosted on the edge router to route a portion of egress traffic to a selected one of the external networks via one of the BGP sessions. A host is configured to route the portion of egress traffic within the public cloud network to the edge router. An edge router configured to route, by the virtual router, the portion of egress traffic from the edge router to the selected one of the external networks.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: Example implementations include a method, apparatus, and computer-readable medium configured for generating a network configuration using a large language model (LLM). The apparatus receives, at an interface between a user and LLM, a natural language intent for a network configuration. The apparatus requests the large language model to update the network configuration to an updated network configuration that satisfies the natural language intent in a declarative network configuration language. The apparatus verifies whether the updated network configuration satisfies a configuration syntax of the declarative network configuration language to detect an error. The apparatus requests the large language model to update the updated network configuration to correct the error. The apparatus deploys the updated network configuration to a user network.

Abstract: This document relates to performing live video stream analytics on edge devices. One example determines resources available to the system, and a video analytics configuration is selected that distributes work between edge devices and cloud devices in a cascading manner, where edge device processing is prioritized over cloud processing in order to conserve resources. This example can dynamically modify the allocation of processing depending on changing conditions, such as network availability.

Abstract: Described are examples for receiving, from one or more second virtual radio access network (vRAN) workloads operating one or more second cells, an indication of a measurement of at least a first signal transmitted by a first vRAN workload operating a first cell, computing, based on measurements of at least the first signal as received from the one or more second vRAN workloads, a boundary of the first cell, and adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.

Abstract: Software-defined networking and network function virtualization constructs are leveraged across diverse portions of 5G network infrastructure including radio access network, mobile core, and wide area network to enable a security property to be implemented for a network slice from end-to-end to provide for strong logical and/or physical isolation of slice traffic from other network traffic. One or more network slice controllers are implemented in the 5G network that are interoperable as separate elements, or under centralized control, to enable the underlying diverse network infrastructure to be abstracted and virtualized so that infrastructure properties can be mapped across infrastructure types for the end-to-end slice.

Abstract: Systems and methods are provided for performing privacy transformation of data to protect privacy in data analytics under the multi-access edge computing environment. In particular, a policy receiver in an edge server receives privacy instructions. Inference determiner in the edge server in a data analytics pipeline receives data from an IoT device and evaluates the data to recognize data associated with personally identifiable information. Privacy data transformer transforms the received data with inference for protecting data privacy by preventing exposure of private information from the edge server. In particular, the privacy data transformer dynamically selects a technique among techniques for removing information that is subject to privacy protection and transforms the received data using the technique.

Abstract: Systems and methods are provided for reusing and retraining an image recognition model for video analytics. The image recognition model is used for inferring a frame of video data that is captured at edge devices. The edge devices periodically or under predetermined conditions transmits a captured frame of video data to perform inferencing. The disclosed technology is directed to select an image recognition model from a model store for reusing or for retraining. A model selector uses a gating network model to determine ranked candidate models for validation. The validation includes iterations of retraining the image recognition model and stopping the iteration when a rate of improving accuracy by retraining becomes smaller than the previous iteration step. Retraining a model includes generating reference data using a teacher model and retraining the model using the reference data. Integrating reuse and retraining of models enables improvement in accuracy and efficiency.

Abstract: Described are examples for providing a system for managing configuration and policies for a virtualized wide area network (vWAN) support on a wide area network (WAN). The vWAN includes a plurality of virtual network entities associated with geographic locations including the physical computing resources of the WAN and virtual connections between the virtual network entities. The system includes a network safety component for managing configurations and policies of the vWAN on the WAN. The network safety component receives a change to a policy or configuration of the vWAN from an operator of a network connected to the vWAN. The network safety component evaluates a set of safety rules for the operator based on the change and a network state of a physical WAN underlying the vWAN. The network safety component generates an error message in response to at least one of the set of safety rules failing the evaluation.

Abstract: A method for controlling transmission power from one or more radio units is provided including monitoring channel state feedback for a signal communicated between a first radio unit of the one or more radio units and a user device in a transmitted frequency range, wherein the channel state feedback is based at least in part on a metric of quality of the communicated radiofrequency signal, determining that the channel state feedback satisfies a channel state condition, wherein the channel state condition includes a metric to evaluate performance of the one or more radio units relative to the user device based at least on the metric of quality of the communicated signal, and transmitting an instruction to adjust a transmission power in the transmitted frequency range of at least one of the one or more radio units based at least on the satisfaction of the channel state condition.

Abstract: Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.