Abstract: A method (200) is disclosed for orchestrating acquisition of a quantity of communication network data for training a target Machine Learning (ML) model for use by a communication network node. The method comprises obtaining a representation of a data acquisition state for the communication network data (210) and using an orchestration ML model to map the representation of the data acquisition state to a first amount of the communication network data to be collected from sources of the communication network data, and a remaining amount of the communication network data to be generated using a generative model (220). The method further comprises, when sufficient data has been collected (240), causing a generative model for the communication network data to be trained using the collected communication network data (250), and causing the remaining amount of the communication network data to be generated using the trained generative model (260).

Abstract: The disclosure relates to methods, a user equipment, a network node and non-transitory computer readable media for protecting a machine learning (ML) model hosted in a network node. The method comprises sending an original request to the ML model hosted in the network node, receiving, from the network node, a request for establishing a secure connection to a post processing module to be installed in a secure enclave of the UE. The method comprises installing the post processing module in the secure enclave and receiving in the secure enclave a response to the original request. The method comprises processing the response to the original request in the secure enclave and obtaining the processed response from the secure enclave, for use by the UE.

Abstract: A method by a first node (111) for handling data. The first node (111) obtains (204) one or more first sets of data corresponding to one or more first features used in a first predictive machine learning, ML, model. The data is annotated with an indication. The indication indicates a respective representation of a distribution of the data. The obtaining (204) is performed before the data are used to train the first ML model. The first node (111) determines (205) whether there has been a change in a respective representation of the distribution of the data, before the data are used to train the first ML model. The first node (111) determines (206) whether to send the data and sends (207) a second indication of the data to a third node (113) in response to the determining (206).

Abstract: There is provided a computer-implemented method for managing a plurality of energy storages at a plurality of sites in a network, the method comprising: acquiring a first dataset including power consumption data of at least a subset of the plurality of energy storages over a predetermined amount of time; generating a first simulated environment of the network based on the acquired first dataset; and training a first reinforcement learning system by performing the following steps iteratively until a termination condition is met; selecting an action from a set of feasible actions, wherein each action in the set of feasible action is bounded by a set of constraints; calculating a reward of the selected action based on the generated first simulated environment of the network; and training the first reinforcement learning system to maximise reward for a given state of the network, based on the calculated reward for the selected action.

Abstract: A node of a wireless communication network receives a transmission of data from a wireless device. Further, the node sends an early acknowledgement message to the wireless device. The early acknowledgement message indicates that the node will handle delivery of the data to a destination device on behalf of the wireless device.

Abstract: A method performed by a computing device in a communication system for management of delivery of power to at least one radio head from a local battery located proximate to the at least one radio head is provided. The method includes determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. The decision made by a machine learning model based on (i) differentiation of output power data statistics including average power and peak power demands, and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization. The method further includes outputting the decision about delivery of power from the local battery to the at least one radio head for the future time window.

Abstract: This document presents one or more advantageous approaches for Reinforcement Learning (RL) powered management of one or more transmission parameters, such as transmit power and diversity, for maximizing the application-layer reliability and availability of a Cyber-Physical System (CPS) with a minimized level of radio/power resource consumption. Example mathematical models are also disclosed and are useful for transforming high-level “intents” (e.g., KPIs that are applicable to industrial automation and control systems) into low-level orchestration objectives that drive the RL-based control. These objectives are subsequently employed in the definition of an RL-powered “orchestrator.” which may comprise an appropriately configured network node or other computing platform associated with the wireless communication network used to provide inter-device communications for a CPS comprising a population of devices. Further, the disclosure details example communication—e.g.