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: Resistance to vulnerabilities from timing-based side-channel attacks on 5G network slices that share underlying physical infrastructure and resources may be enhanced by selectively imposing time-based constraints on service provisioning and data handling to obscure data-driven time variations that occur during workload execution in a slice that can leak secret information. By preventing timing leakage from the 5G network slices, an attacker cannot observe execution latencies to thereby infer the constituency of workload characteristics. In addition, the attacker cannot create contention for shared resources on its own slice to observe an extent to which the shared resources are utilized by a targeted slice.

Abstract: Described are examples for calculating and exposing network capacity and congestion to applications. A network entity such as a radio access network (RAN) intelligent controller (RIC) or virtual base station component receives measurements of a signal quality for a plurality of user devices connected to a RAN. The network entity estimates a deliverable throughput of a wireless link for a user device of the plurality of user devices based on at least the measurements. The network entity can consider other factors such as a number of competing users, queue sizes of the user device and of the competing users, or a scheduling policy. The network entity provides the deliverable throughput to an application server for an application of the user device communicating with the application server via the RAN. The application server can adapt a data rate for the application and the user device based on the deliverable throughput.

Abstract: The devices and methods leverage harmonics to resolve, separate, and identify devices. The devices and methods use the harmonic patterns associated with a frequency modulating (FM) signal to discern and extract information from the FM signal using correlation learning in a crowded spectrum space where the nodes are transmitting simultaneously on multiple channels. The methods and devices leverage harmonics to resolve, separate, and/or identify wireless communication devices.

Abstract: Resistance to vulnerabilities from timing-based side-channel attacks on 5G network slices that share underlying physical infrastructure and resources may be enhanced by selectively imposing time-based constraints on service provisioning and data handling to obscure data-driven time variations that occur during workload execution in a slice that can leak secret information. By preventing timing leakage from the 5G network slices, an attacker cannot observe execution latencies to thereby infer the constituency of workload characteristics. In addition, the attacker cannot create contention for shared resources on its own slice to observe an extent to which the shared resources are utilized by a targeted slice.

Abstract: A method for utilizing computing resources in a vRAN is described. A predicted resource load is determined for data traffic processing of wireless communication channels served by the vRAN using a trained neural network model. The data traffic processing comprises at least one of PHY data processing or MAC processing for a 5G RAN. Computing resources are allocated for the data traffic processing based on the predicted resource load. Wireless parameter limits are determined for the wireless communication channels that constrain utilization of the allocated computing resources using the trained neural network model, including setting one or more of a maximum number of radio resource units per timeslot or a maximum MCS index for the wireless parameter limits. The data traffic processing is performed using the wireless parameter limits to reduce load spikes that cause a violation of real-time deadlines for the data traffic processing.

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.