Abstract: Methods and apparatus are provided. In an example aspect, a method of determining allocation of unmanned aerial vehicle (UAV) base stations in a wireless network is provided. The method further comprises expressing determination of allocation of UAV base stations to a one or more wireless communication devices in the wireless network as a quadratic unconstrained binary optimization (QUBO) problem, and executing the QUBO problem on a quantum computing device to determine the allocation of UAV base stations to a one or more wireless communication devices in the wireless network.

Abstract: Methods and apparatus are provided. In an example aspect, a method of determining a subset of base stations, BSs, in a wireless network is provided. The method comprises forming a minimization problem, wherein the minimization problem represents a problem 5 of determining a subset of BSs in the wireless network to which one or more user equipments, UEs, in the wireless network can connect, so as to maximize a measure of efficiency of the wireless network, and executing the minimization problem using a quantum computing device to determine the subset of BSs.

Abstract: Methods and apparatus are provided. In an example aspect, a method of determining a subset of base stations, BSs, in a wireless network is provided. The method comprises forming a minimization problem, wherein the minimization problem represents a problem 5 of determining a subset of BSs in the wireless network to which one or more user equipments, UEs, in the wireless network can connect, so as to maximize a measure of efficiency of the wireless network, and executing the minimization problem using a quantum computing device to determine the subset of BSs.

Abstract: A method for allocating resources of fog nodes is disclosed, wherein the fog nodes are organized into at least one fog network. The method includes receiving a request from a client node, identifying requirements for fulfillment of the request, determining a location of the client node for fulfillment of the request, and identifying, from the identified requirements and the determined location, a cluster of fog nodes operable to fulfill the request. The method further includes selecting, from the identified cluster, fog nodes the resources of which are to be allocated to fulfilling the request by minimizing at least one of a number of clusters required to fulfill the request, a total time to fulfill requests received by the fog nodes, and/or a number of unfulfilled requests received by the fog nodes.

Abstract: Methods and apparatus are provided. In an example aspect, a method of determining allocation of unmanned aerial vehicle (UAV) base stations in a wireless network is provided. The method further comprises expressing determination of allocation of UAV base stations to a one or more wireless communication devices in the wireless network as a quadratic unconstrained binary optimization (QUBO) problem, and executing the QUBO problem on a quantum computing device to determine the allocation of UAV base stations to a one or more wireless communication devices in the wireless network.

Abstract: A computer-implemented method for building a machine learning (ML) model is provided. The method includes training a ML model using a set of input data, wherein the ML model includes a plurality of layers and each layer includes a plurality of filters, and wherein the set of input data includes class labels; obtaining a set of output data from training the ML model, wherein the set of output data includes class probabilities values; determining, for each layer in the ML model, by using the class labels and the class probabilities values, a working value for each filter in the layer; determining, for each layer in the ML model, a dominant filter, wherein the dominant filter is determined based on whether the working value for the filter exceeds a threshold; and building a subset ML model based on each dominant filter for each layer, wherein the subset ML model is a subset of the ML model.

Abstract: Methods and apparatus are provided. In an example aspect, a method of determining allocation of resources in a wireless network is provided. The method includes expressing determination of allocation of resources for a plurality of wireless communication devices in the wireless network as a quadratic unconstrained binary optimization (QUBO) problem, and executing the QUBO problem on a quantum computing device to determine the allocation of resources to the plurality of wireless communication devices in the wireless network.

Abstract: Embodiments described herein relate to a quantum computing device, methods and apparatus for determining a group of interconnected nodes in a network parameter. In an embodiment, a method includes using a quantum computing device to determine initial groups of adjacent nodes based on maximising modularity, and detecting a group of interconnected nodes by grouping the initial groups of adjacent nodes based on maximising modularity.

Abstract: Managing network event data in a telecommunications network A method (200) is disclosed for managing network event data in a telecommunications network, wherein a network event is associated with a plurality of network event data fields, and wherein the telecommunications network comprises a data storage facility for network event data, the data storage facility comprising a plurality of storage functions. The method comprises obtaining queries submitted to the data storage facility (210) and, for a network event data field, determining a frequency with which data in the network event data field is required in order to respond to the obtained queries (220) and using a trained machine learning model to map the determined frequency to a value of a selection parameter for the network event data field (230).

Abstract: A method for allocating resources of fog nodes is disclosed, wherein the fog nodes are organized into at least one fog network. The method includes receiving a request from a client node, identifying requirements for fulfillment of the request, determining a location of the client node for fulfillment of the request, and identifying, from the identified requirements and the determined location, a cluster of fog nodes operable to fulfill the request. The method further includes selecting, from the identified cluster, fog nodes the resources of which are to be allocated to fulfilling the request by minimizing at least one of a number of clusters required to fulfill the request, a total time to fulfill requests received by the fog nodes, and/or a number of unfulfilled requests received by the fog nodes.