Abstract: A method includes receiving, by a first user device from a network node, an indication for the first user device to act as a user device-network relay; and, in response to the receiving: carrying out a discovery procedure for determining at least one second user device that is capable of sidelink communications and utilizes machine learning; receiving, by the first user device from the network node, machine learning configuration information; and transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information for carrying out a machine learning related operation.

Abstract: There are provided measures for enabling/realizing efficient model training, including model collection and/or aggregation, for federated learning, including hierarchical federated learning, in a wireless communication system. Such measures exemplarily comprise that a federated-learning training host configured for local model training decides on how to perform the local model training depending on availability of a cluster head and computation and communication costs for a federated-learning training task, and either locally performs the local model training or delegates at least part of a federated-learning training task to the cluster head. Also, such measures exemplarily comprise that a federated-learning training host configured for local model training computes a similarity metric between a locally computed set of local model parameters and each the received sets of local model parameters, and decides on whether to operate as a temporary cluster head for one or more federated-learning training hosts.

Abstract: Systems, methods, apparatuses, and computer program products for testing user equipment (UE) machine learning-assisted radio resource management (RRM) functionalities are provided. One method may include selecting a radio resource management (RRM) functionality to be tested for a user equipment (UE) having advertised machine learning (ML)-assistance capabilities, initializing a machine learning (ML)-assistance model in the user equipment based on the advertised machine learning (ML)-assistance capabilities, generating one or more input test signals and corresponding reference output test conditions depending on the machine learning (ML)-assistance radio resource management (RRM) functionality under test, and activating UE machine learning (ML)-assistance functionality and provisioning, to the user equipment, a test sequence with the generated input test signals and corresponding reference output conditions.

Abstract: Systems, methods, apparatuses, and computer program products for cluster based local ML model training host selection in asynchronous federated learning model collection. A method may include transmitting local model parameters following training of a model of at least one network element to a network node to be used to cluster the at least one network element with one or more other network elements. The method may also include training, by one or more network elements selected from the cluster, an aggregated model using the local model parameters. The method may further include transmitting, by the one or more network elements selected from the cluster, updated local model parameters of the at least one network element as a result of the training to the network node.

Abstract: An apparatus of a first communication node is provide that includes: means for synchronising a common reference timing with a second communication node; means for obtaining an indication of a time window that specifies a period of time between first and second time instances; and means for configuring a machine learning-based function at the first communication node, wherein the configuration of the machine learning-based function is common between the first and second communication nodes. The apparatus further includes means for executing the machine learning-based function; and means for obtaining information by measuring a performance metric, for the machine learning-based function, during the time window.

Abstract: There is provided a method for a UE of a wireless communication system, the method comprising: detecting an imbalance in a line-of-sight, LOS, training dataset for training a LOS detector; determining a minority class of the LOS training dataset associated with the detected imbalance; transmitting, to a network element of the wireless communication system, a request message indicating the determined minority class and requesting measurement activation to perform one or more measurements, by the UE, to obtain one or more additional signal samples for the minority class; and receiving, from the network element, an activation message causing the UE to perform the one or more measurements during a configured observation window.

Abstract: Apparatuses and methods for communication in non-terrestrial networks are provided. Downlink transmission from a satellite node is received (300). Pathloss of uplink transmission to the satellite node is estimated (302). Based on the path loss, it is determined (304) whether uplink transmission of the apparatus can reach the satellite node and uplink transmission suspended (306) if determination indicates that the transmission will not reach the node.

Abstract: A technique comprising: receiving, at a data analytics function of a core network of a mobile communication system, travel path data from a traffic management entity, wherein the travel path data indicates a travel path for a vehicle comprising a user equipment registered to the mobile communication system; receiving, at the data analytics function, location data for the vehicle transmitted by the user equipment; and in response to detecting an inconsistency between the travel path data for the vehicle and the location data for the vehicle transmitted by the user equipment, outputting a travel path deviation report for the traffic management entity.

Abstract: According to an example embodiment, a client device comprises at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least ne processor, cause the client device to: detect need for random access, random access, in a non-terrestrial network to a target cell of the non-terrestrial network; identify whether the needed random access is intra-satellite or inter-satellite; in response to the needed random access being intra-satellite, perform a first random access procedure, wherein, in the first random access procedure, the client device transmits a random access preamble and a payload data in a single message; and in response to the needed random access being inter-satellite, perform a second random access procedure wherein, in the second random access procedure, the client device transmits a random access preamble and a payload data in separate messages.

Abstract: There is provided an apparatus, said apparatus comprising means for determining which of at least two lower layer radio units co-located at a first location, each lower layer radio unit associated with a higher layer radio unit, has a best path to the associated higher layer radio unit and causing the transmit power of the determined one of the at least two lower layer radio units received at a user equipment to be higher relative to the transmit power of any of the other of the at least two lower layer radio units received at the user equipment.

Abstract: An apparatus, computer program and method is described comprising: obtaining, at a receiver, during a first phase of operation, one or more first downlink positioning reference samples from each of a plurality of communication nodes of a mobile communication system, wherein the receiver operates in a wide-beam or omnidirectional mode during the first phase of operation; determining first response estimates of channels between the receiver and each of said each communication nodes, based on the positioning reference samples obtained for the respective communication node; selecting, for each communication node, a receiver beam for receiving positioning signals from the respective communication node, based, at least in part, on the respective first channel response estimate; and receiving, at the receiver, in a second phase of operation, one or more second downlink positioning reference samples from each of the plurality of communication nodes using the selected receiver beams.

Abstract: A method may include sending, by a user equipment, a random access preamble message to a network element. The method may also include receiving an uplink grant in a random access response message in response to the random access preamble message. The method may further include adding an implicit timing advance offset to an uplink time domain resource allocation of the uplink grant to establish synchronization of radio transmissions between the user equipment and the network element. The method may also include transmitting a scheduled transmission message including the implicit timing advance offset to the network element. Further, the implicit timing advance offset may be added to the uplink time domain resource before the scheduled transmission message is transmitted.

Abstract: According to an example embodiment, a client device comprises at least one processor and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the client device to: receive a measurement configuration from a first network node device, receive a preconfigured condition for the measurement configuration from the first network node device, and in response to the preconfigured condition for the measurement configuration being fulfilled, use the measurement configuration. A client device, a network node device, methods, and computer programs are disclosed.

Abstract: A method, apparatus and computer program is described comprising: obtaining reference signals received at a plurality of nodes of a communication system from a user device; generating signal signature matrices based on real and imaginary components of the obtained reference signals; and generating a first three-dimensional position estimate for the user device by applying signals based on the generated signal signature matrices to an input of a model.

Abstract: An apparatus for use by a communication network control element or function configured to conduct a communication control for a communication with at least one communication element or function in a communication network, the apparatus comprising at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least: to calculate a reception time window in a random access procedure for a communication element or function in at least one communication area controlled by the communication network control element or function, to provide setting information related to a random access procedure to a communication element or function in the at least one communication area, the setting information including an indication of the calculated reception time window and an indication related to a determination of a receivin

Abstract: Disclosed is a method comprising using a machine learning algorithm to select an antenna panel from a plurality of antenna panels. A first long-term reward value associated with the selected antenna panel is determined based at least partly on one or more first signals received on the selected antenna panel. A second signal is then transmitted or received via the selected antenna panel, if the first long-term reward value exceeds one or more second long-term reward values associated with at least a subset of the plurality of antenna panels.

Abstract: A method comprising, at a user device, comprising obtaining a target parameter representative of a handover rate from a base station, comparing said target parameter representative of a handover rate with a measured parameter representative of a handover rate, adjusting at least one measurement trigger event parameter as a result of said comparison in a direction tending to reduce the difference between said two rates, sending a measurement report to said base station if a trigger condition using said at least one adjusted trigger event parameter is met.

Abstract: This document discloses a solution for providing a terminal device with one or more candidate cells for link setup. A network node providing the candidate cell(s) may compute a validity window for each candidate cell and indicate the validity window(s) to the terminal device.

Abstract: An apparatus and method that may receive, at an unmanned aerial vehicle, at least one coordinate from a base station, wherein the at least one coordinate comprises one or more reference altitude values associated with at least one of an altitude of an antenna associated with the base station or a ground altitude of the base station. The method may further determine that the unmanned aerial vehicle is within a first three-dimensional zone of a plurality of three-dimensional zones based on the one or more reference altitude values. The method may further adjust mobile network settings of the unmanned aerial vehicle based on the determined first three-dimensional zone.

Abstract: Various communication systems may benefit from an improved exchange of information pertaining to an unmanned aerial vehicle (UAV). For example, certain communication systems may benefit from the transmittal of information related to a planned route of the UAV to a cellular network. A method, in certain embodiments, may include receiving a connection establishment request at a network entity from a traffic management system of an unmanned aerial vehicle. The method may include at least one of location information or timing information of a planned route for the unmanned aerial vehicle. The method may also include establishing a connection between the unmanned aerial vehicle and a cellular network via the network entity based on the connection establishment request.