Abstract: The present disclosure relates to a machine-learning (ML) orchestrator entity that provides distributed, flexible, and efficient parameter initialization for ML agents installed on network nodes operating under similar radio conditions. For this end, the ML orchestrator entity instructs two or more of the network nodes to run two or more ML agents in a training mode, which results in generating two or more sets of parameters. Then, the ML orchestrator entity uses the sets of parameters to derive a common set of parameters for the network nodes. The common set of parameters is to be used in an inference mode of the ML agent at each of the network nodes. The transmission of the common set of parameters to the network nodes may be subsequently initiated by the ML orchestrator entity itself or by each of the network nodes independently.

Abstract: A method including receiving a configuration indicating one or more rules for switching between a set of operational modes associated with at least one of: inference, data collection or training of a machine learning model, the machine learning model being associated with a network optimization function; determining, based on the one or more rules, whether to switch from a current operational mode to another operational mode from the set of operational modes; and performing the current operational mode or the other operational mode based on the determination.

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: Devices, methods and computer programs for timing advance variation compensation in wireless communications are disclosed Timing advance variation for a client device communicating with a network node device at least partially via an air/space born vehicle communication connection is determined based on measurements on uplink signaling from the client device. A timing advance compensation function curve from a set of predetermined timing advance compensation function curves is selected, such that the selected timing advance compensation function curve corresponds with the determined timing advance variation. An indication of the selected timing advance compensation function curve is signaled to the client device.

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: Various example embodiments relate to a solution for modifying mobility events by making them depend on information which captures a moving cell direction. A network node may obtain coverage information associated with a serving network and a target network. The network node may obtain synchronization signal timing data associated with the target network. The network node may determine time drift rate data for at least one user node based at least partly on location data and satellite ephemeris data. The network node may cause transmission of the time drift rate data associated with the time drift to the at least one user node to enable the user node to perform a measurement of a target cell of the target network using the time drift rate data. Nodes, methods, and computer programs are disclosed.

Abstract: Techniques of updating ML models include performing such an update when the UE satisfies certain criteria. In some implementations, the ML model is used by the UE to determine a location within a network. In some implementations, the criteria include a version number of the ML model being used by the UE. In some implementations, the criteria include a time elapsed since a last ML model update was provided to the user equipment.

Abstract: An apparatus, method and computer program is described comprising: determining, at a user device of a mobile communication system, one or more parameters of a serving cell and one or more target cells at a first time instance; and estimating one or more time delay metrics for each of the one or more target cells based on said parameters, such that an optimal time point for performing a handover can be determined. The time delay metrics comprise one or more of: a first time delay corresponding to a time period between the first time instance and a time instance at which a signal strength of the serving cell is predicted to match a signal strength of the respective target cell; and a second time delay corresponding to a time period between the first time instance and a time instance at which the signal strength of the respective target cell is predicted to be higher than the signal strength of the serving cell by an offset value.

Abstract: A method includes confirming, by a user device with a network node, a set of machine learning (ML) functionality adaptation parameters for the user device to perform adaptation of a ML functionality associated with at least one ML model that is used by the user device to perform a radio access network (RAN)-related function. The set of ML functionality adaptation parameters indicate at least one adaptation cycle during which the user device is to perform the ML functionality adaptation and a validity period for which the set of ML functionality adaptation parameters are valid. The method also includes performing, by the user device, adaptation of the ML functionality during the at least one adaptation cycle.

Abstract: A method, apparatus and computer program product are provided for cell change assistance in a non-terrestrial network. NTN, with a plurality of moving cells served by at least one satellite. A network element of the NTN provides cell change assistance information to a user equipment, UE, for selecting at least one moving cell of the plurality of moving cells. The UE receives cell change assistance information for selecting a cell for cell change. The cell change assistance information identifies at least a subset of the plurality of moving cells and us indicative of a geometric layout of at least the subset of the plurality of moving cells.

Abstract: An apparatus configured to: enter a test mode; receive, from a test equipment, an indication of at least one first model used at a first side of a two-sided model; request, from a model repository, at least one second model for use at a second side of the two-sided model, wherein the at least one second model is selected based, at least partially, on the at least one first model; and receive, from the model repository, the at least one second model. An apparatus configured to: receive, from a test equipment, a model validation request; provide a data set to at least one model to obtain an inference validation response, wherein the at least one model is used at a first side of a two-sided model; transmit, to the test equipment, the inference validation response; and receive, from the test equipment, a validation sequence result.

Abstract: There is provided an apparatus comprising means for communicating a travel path plan of the apparatus to a communication network; means for receiving from the communication network information of one or more cells of the communication network associated with priorities of accessing; and means for using the received information for adjusting priorities for measurements towards different cells of the network. There is also provided a method and a computer program product.

Abstract: Embodiments of the present disclosure relate to conditional handover. A method comprises: upon a successful handover to a first target cell in an ordered list of target cells configured for a terminal device to initiate handover in order, determining, at the terminal device, information for assessing a validity of the ordered list; and transmitting, to a first network device providing the first target cell, a first message comprising the information for assessing the validity of the ordered list. In this way, a chain of target cells is capable of preparing for the future handover from the suitable time, which avoids reserving resources too early or too late and improves the resource efficiency. Moreover, the current serving cell can detect the deviation of the terminal device from the chain in time, thus a configuration of the ordered list can be appropriately adjusted or updated.

Abstract: According to an aspect, there is provided an apparatus for performing the following. The apparatus performs cell-specific radio measurements for a plurality of cells or beam-specific radio measurements for a plurality of transmit beams. The apparatus evaluates correlation between results of the cell/beam-specific radio measurements and compares the correlation against one or more pre-defined correlation conditions. In response to the correlation for at least one pair of cells in the plurality of cells or for at least one pair of transmit beams in the plurality of transmit beams satisfying the one or more pre-defined correlation conditions, the apparatus filters the results of the cell-specific or beam-specific radio measurements to remove repetitions of results satisfying the one or more pre-defined correlation conditions and transmits, to another apparatus, a measurement report comprising the filtered results and information on the correlation.

Abstract: There is provided an apparatus, said apparatus comprising means for receiving from a network, at a user equipment, Doppler shift information associated with at least one cell of the network as a function of time for at least one area and using the Doppler shift information at the user equipment to compensate for the Doppler shift in communication with the network when the user equipment is in the at least one area.

Abstract: An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element.

Abstract: A method, apparatus, and a computer-readable storage medium are provided for proactive triggering of handovers in wireless communication networks.

Abstract: In accordance with example embodiments of the invention there is at least a method an apparatus to perform receiving or sending, between a network node of a communication network, and a user equipment information comprising a service request for a dedicated bearer for data exchange of at least one of machine learning or artificial intelligence related data, wherein the data exchange is for training at least one of a machine learning or an artificial intelligence model for a particular use case; communicating the information with the communication network; handling control over at least one of an artificial intelligence or machine learning related data exchange through a machine learning-dedicated bearer, wherein the artificial intelligence or machine learning related data exchange comprises: data collection, model transfer, and life cycle Management for at least one of a machine learning model or machine learning functionality control signalling.

Abstract: An apparatus, method and computer program is described comprising: receiving antenna panel switching rate configuration parameters; determining an antenna panel switching rate at a user device of a communication system, wherein the antenna panel switching rate is based on a number of antenna panel switching events within a moving averaging window, wherein the window has a size defined by said configuration parameters; and providing a panel switching rate report to a network node of the communication system, wherein the panel switching rate report includes the determined antenna panel switching rate.

Abstract: According to an example embodiment, a network node device is configured to generate at least one client cluster comprising at least one client device served by the network node device according to at least one clustering criterion; assign the at least one control algorithm instance to the at least one client cluster; control at least one transmission power control parameter of the at least one client device in the at least one client cluster using the at least one control algorithm instance.