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: 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: The techniques disclosed herein provide a system for constructing an automated telecommunications network operation model prior to deployment in a telecommunications network for completing downstream tasks. In general, the performance of artificial intelligence agents such as large language models can degrade when applied to highly specific and/or complex domains such as telecommunications network operations resulting in erroneous outputs and potentially leading to network outages. As such, the present techniques finetune a large language model using a domain specific dataset to establish a specialized context directed to telecommunications network operations. That is, the large language model is pre-trained to establish the specialized context prior to deployment in the operation of a telecommunications network.

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: 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: 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: 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 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: 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: 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: Systems and methods are provided for continuous learning of models across hierarchies under a multi-access edge computing. In particular, an on-premises edge server, using a model, generates inference data associated with captured stream data. A data drift determiner determines a data drift in the inference data by comparing the data against reference data generated using a golden model. The data drift indicates a loss of accuracy in the inference data. A gateway model maintains one or more models in a model cache for update the model. The gateway model instructs the one or more servers to train the new model. The gateway model transmits the trained model to update the model in the on-premises edge server. Training the new model includes determining an on-premises edge server with computing resources available to train the new model while generating other inference data for incoming stream data in the data analytic pipeline.

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 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.