Abstract: The present disclosure relates to a system for use in a wireless network, the system including: a processor configured to determine a performance parameter representative of a performance of a model of radio resource management, the model operating on a radio access network environment; and a radio resource manager configured to perform radio resource management dependent on the performance parameter.

Abstract: A device may include a processor. The processor may sample a portion of a workload to offload to compute resources and a portion of a current workload of the compute resources. The processor may simulate offloading of the workload to the compute resources using the sampled portion of the workload, the sampled portion of the current workload, and telemetry data corresponding to the compute resources. The compute resources may be configured to perform the workload according to a plurality of offloading configurations. The processor may determine a rank score for each offloading configuration of the plurality of offloading configurations based on the simulations. Responsive to a rank score corresponding to an offloading configuration of the plurality of offloading configurations exceeding a threshold value, the processor may offload the workload to the compute resource corresponding to the offloading configuration that corresponds to the rank score that exceeds the threshold value.

Abstract: An apparatus of a wireless device, such as a user equipment (UE), can include processing circuitry configured to perform one or more of the handover-related techniques disclosed herein. For example, when associated with moving with a plurality of mobile devices from coverage of a first cell to a second cell, the processing circuitry can detect the second cell. One or more parameters of the second cell can be measured. The one or more parameters can be communicated to one or more other mobile devices of the plurality of mobile devices.

Abstract: The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

Abstract: In one embodiment, a machine learning (ML) model for determining radio resource management (RRM) decisions is updated, with ML model parameters being shared between RRM decision makers to update the model. The updates may include local operations (between an AP and UE pair) to update local primal and dual parameters of the ML model, and global operations (between other devices in the network) to exchange/update global parameters of the ML model.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to cause an initial set of weights for a global machine learning (ML) model to be transmitted a set of client compute nodes of the edge computing network; process Hessians computed by each of the client compute nodes based on a dataset stored on the client compute node; evaluate a gradient expression for the ML model based on a second dataset and an updated set of weights received from the client compute nodes; and generate a meta-updated set of weights for the global model based on the initial set of weights, the Hessians received, and the evaluated gradient expression.

Abstract: Embodiments of the present disclosure are directed towards devices and methods for identifying preferred access networks based at least in part on access network information including access network assistance information, steering policies, or access commands. In some embodiments, conflicts between access network information and access network discovery and selection function (ANDSF) policies may be rectified in identifying a preferred access network.

Abstract: Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: A central trajectory controller including a cell interface configured to establish signaling connections with one or more backhaul moving cells and to establish signaling connections with one or more outer moving cells, an input data repository configured to obtain input data related to a radio environment of the one or more outer moving cells and the one or more backhaul moving cells, and a trajectory processor configured to determine, based on the input data, first coarse trajectories for the one or more backhaul moving cells and second coarse trajectories for the one or more outer moving cells, the cell interface further configured to send the first coarse trajectories to the one or more backhaul moving cells and to send the second coarse trajectories to the one or more outer moving cells.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to manage a self-adaptive heterogeneous emergency network. An example apparatus to establish recovery nodes includes failure detection circuitry to determine a node initiated a reset procedure, override circuitry to suppress a native recovery procedure of the node, formation circuitry to initiate a heterogeneous recovery procedure, and trust circuitry to measure a root of trust of the node. Further, the example apparatus instantiates the formation circuitry further to broadcast heterogeneous recovery packets, and activate listener ports for responses to the heterogeneous recovery packets.

Abstract: Methods, apparatus, and computer-readable media are described to use multi-finger beamforming for multimeter wave communications. A base station associates with first and second user equipment. Weight sum rates are determined for the user equipment. Transmissions are scheduled to the user equipment based on the weight sum rates. Data is encoded for the first user equipment and transmitted based on the schedule. Data is encoded for the second user equipment and transmitted based on the schedule. The transmissions are multiplexed in the power domain.

Abstract: An apparatus of a transmitter computing node n (TX node n) of a wireless network, one or more computer readable media, a system, and a method.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Abstract: Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Abstract: Disclosed herein is a communication device for vehicular radio communications. The communication device includes one or more processors configured to identify a plurality of vehicular communication devices that form a cluster of cooperating vehicular communication devices. The one or more processors also determine channel resource allocations for the plurality of vehicular communication devices that includes channel resources allocated for a first vehicular radio communication technology and channel resources allocated for a second vehicular radio communication technology. The one or more processors also transmit the channel resource allocation to the plurality of vehicular communication devices.