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.

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: Provided are aspects relating to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

Abstract: Aspects of the present disclosure relate to determining reference symbol transmission times. In some examples, a method for determining reference symbol transmission times for cellular communications includes receiving signal feedback based on a wireless communication channel between a wireless communication device and a base station, identifying a periodic exchange of reference symbols that are used to adjust beamforming between the wireless communication device and the base station, generating a vector based on the signal feedback, and providing the vector as an input to a trained machine learning model. A training of the trained machine learning model includes calculating a plurality of rewards for a respective plurality of transmission time delays. The plurality of rewards are each calculated based on a function of downlink throughput and uplink overhead. The function of downlink throughput and uplink overhead are based upon a priority level of the wireless communication device.

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: Described are examples for machine learning based interference detection for tiered licensing deployments. A network entity in a general authorized access (GAA) deployment checks a local spectrum access database of GAA users to determine that a portion of shared use spectrum is free from known local users in a geographic area. The network entity receives samples of a wireless signal including at least a desired signal on the portion of shared use spectrum. The network entity determines whether the wireless signal includes multiple independent signals. The network entity identifies an interfering signal in response to determining that the wireless signal includes multiple independent signals.

Abstract: Described are examples for repurposing mobility management with virtual radio in software radio access networks. A virtual mobile network includes a first server configured to host a first mobile network distributed unit (DU) for providing a first virtual cell to a plurality of user devices via a radio unit. The virtual mobile network also includes a second server configured to host a second mobile network distributed unit providing a second virtual cell via the same radio unit. A radio access network (RAN) intelligent controller (RIC) is configured to control the first DU and the second DU to hand over the plurality of user devices from the first virtual cell to the second virtual cell. The first server may then be shut down for maintenance or updates without dropping service to the user devices.

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. For instance, transformations on messages between the L2 and PHY, such as duplication and filtering, enable the system to maintain one or more low-overhead “hot, inactive” PHY clones. A hot, inactive PHY clone may be a duplicate of an operational PHY, where the PHY clone is primed to process a PHY workload of the operational PHY (e.g., “hot”) but is not currently responsible for processing the PHY workload (e.g., low-overhead, inactive). In this way, a PHY workload may be automatically and seamlessly migrated to the hot PHY clone in response to planned downtime (e.g., scheduled maintenance, software upgrades) or unexpected events (e.g., server failures) within the strict transmission time intervals (TTIs) required for processing the PHY workload.

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: In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.

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: Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.

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: 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: Systems and methods for routing packet data for transmission via a plurality of communication links are described. A method may include dividing a usage cycle for the plurality of communication links into a plurality of timeslots. Packet data traffic demands for the packet data for transmission via the plurality of communication links may be received. Based on a mixed integer linear programming model, an allocation of the packet data traffic demands to the plurality of communication links during the usage cycle may be determined using binary constraints of the mixed integer linear programming model. The binary constraints may prioritize respective subsets of the plurality of timeslots for at least some of the plurality of communication links. For each of the plurality of timeslots, an allocation of the packet data traffic demands to each of the plurality of communication links may be determined using the mixed integer linear programming model.

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: Described are examples for providing management of a virtual wide area network (vWAN) based on operator policies. A network orchestrator presents, to a network operator, a representation of the vWAN including virtual network entities associated with respective geographic locations and virtual connections between the virtual network entities. The network orchestrator receives a policy for the virtual wide area network from the network operator via the representation, the policy to be implemented at one or more of the virtual connections. The network orchestrator translates the policy for the virtual wide area network into a configuration of an underlying wide area network (WAN). The underlying WAN a plurality of geographically distributed physical computing resources in geographic regions corresponding to the virtual network entities and connections there between.

Abstract: The present application relates to communications between a partner network and a wide area network (WAN) via the Internet. Although Internet service providers may act as autonomous systems, the WAN may control routing from the partner network by advertising unicast border gateway protocol (BGP) address prefixes for a plurality of front-end devices in the WAN. An agent in the partner network measures a plurality of paths to a service within the WAN. Each of the plurality of paths is associated with one of the plurality of front-end devices and a respective unicast BGP address prefix. The WAN selects a path within the WAN for the service. The WAN exports a routing rule to the agent. The agent forwards data packets for the service to the respective BGP address prefix via the Internet. The WAN receives data packets for the service of the partner network at the selected device.

Abstract: In a 5G network, a profiler component of a network slice controller is arranged to dynamically observe behaviors of pre-defined types of network slices when handling current traffic. The profiler employs the observed behaviors to generate profiles of the pre-defined slice types in terms of throughput, reliability, or other suitable metrics. In response to a request from an application for admission to the 5G network for which an ID of an appropriate pre-defined network slice type is unknown, the application request and traffic is handled on a slice which is temporarily utilized while the profiler dynamically observes application behaviors to generate an application profile. The profiler identifies a pre-defined slice type having a profile that is the closest match to the generated application profile. The application may then be moved from the temporary slice to a slice of the identified pre-defined type so that optimal slice characteristics are provided for the application's traffic.