Abstract: Systems, methods, apparatuses, and computer program products for indicating validity as a function of confidence level. A requesting network entity may receive a prediction request from a requesting network entity that comprises timing information indicating when a prediction needs to be obtained. The receiving entity may then transmit a response to the requesting network entity associated with the requested prediction.

Abstract: Examples of the present disclosure relate to management of Listen-Before-Talk. Certain examples provide a centralized unit 210 of an access node 120 for use in a network comprising at least the centralized unit 210 and a distributed unit 220, the centralized unit 120 comprising means configured to: receive information 301 indicative of a quality of a channel; and cause an adaptation 302 of a Listen-Before-Talk state 501 of at least one network node based 220/110, at least in part, on the received information 301.

Abstract: An apparatus comprising: means for establishing a connection to a network; means for predicting, by an algorithm-based model implemented in said apparatus, whether any of configurable features of the apparatus infers a predetermined likelihood to cause an anomalous operating condition in said apparatus; and means for signalling information about said configurable features to the network.

Abstract: An apparatus configured to: obtain an indication of partitions of a machine learning model corresponding to respective ones of the one or more groups; transmit, to the respective ones of the plurality of user equipments, a corresponding partition, of the partitions of the machine learning model; transmit, to the plurality of user equipments, an indication to record measurements for the first cell; receive, from at least one of the plurality of user equipments, at least one message regarding a handover failure, wherein the at least one message comprises a message generated using a first partition of the partitions of the machine learning model; and determine, with a second partition of the partitions of the machine learning model, whether the first cell is a rogue base station based, at least partially, on a plurality of detection reports.

Abstract: Apparatus comprising: one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform: monitoring whether a terminal suffers a performance issue due to an artificial intelligence/machine learning operation performed by the terminal; performing an action related to the artificial intelligence/machine learning operation to remove or reduce the performance issue if the terminal suffers the performance issue due to the artificial intelligence/machine learning operation.

Abstract: Method comprising: providing, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model; monitoring whether the first node receives, from the second node for at least one of the one or more machine learning models, a configuration request requesting to configure the respective machine learning model according to a respective requested configuration; configuring the at least one of the one or more machine learning models according to the respective requested configuration if the first node receives the configuration request for the at least one of the one or more machine learning models from the second node.

Abstract: A method including: receiving, by a multi-panel user equipment from a serving network entity, information indicative of predicted transmissions of reference signals by a serving network entity or target network entity; and applying a panel prioritization strategy for the multi-panel user equipment based on the received information indicative of predicted transmissions of reference signals.

Abstract: A communications network comprising network nodes (20), one network node being configured to generate a requests (200) requesting predicted data from at least one other network node. An other network node being configured in response to receiving a request for predicted data, to generate the predicted data and to transmit (220) the predicted data to the requesting network node.

Abstract: According to an aspect, there is provided an apparatus comprising a data collection configuration for carrying out data collection measurements for training an artificial intelligence or machine learning model used in controlling a mode change in a first radio cell to an energy saving mode.

Abstract: Systems, methods, apparatuses, and computer program products for distinguishing minimization of drive tests (MDT) logging through different radio resource controller (RRC) states. One method may include receiving, from a network element, a logged measurement configuration comprising specific content for a user equipment depending on a connection state of the user equipment. The method may also include validating the logged measurement configuration, wherein the validating is embodied by the network element prior to transitioning to a different user equipment state, or by a user equipment after entering a logging state, for which the logged measurement configuration was designated. The method may further include performing logging of a measurement according to the content of the logged measurement configuration. In addition, the method may include tagging the logged measurement. The method may also include transmitting the tagged logged measurement to the network element.

Abstract: There are provided measures for enablement of federated machine learning for terminals to improve their machine learning capabilities. Such measures exemplarily comprise, at a terminal, receiving a configuration indicative of an instruction to participate in federated learning of a global machine learning model, the configuration including timing information related to said federated learning, and performing, based on said configuration, a machine learning process based on undertaken network performance related measurements, wherein said timing information includes a time limit with respect to a local machine learning model resulting from said machine learning process, and said time limit is a specification of a moment in time by when said local machine learning model is to be completed or a specification of a moment in time by when transmission of said local machine learning model is to be completed, and wherein said configuration is a minimization of drive tests configuration.

Abstract: There is provided an apparatus, said apparatus comprising means for determining for at least one of a plurality of user equipments, based on an activation probability value of some or all of the plurality of user equipments, uplink resources for use by the at least one user equipment; and providing an indication of the determined uplink resources to the at least one user equipment.

Abstract: Disclosed is a solution for managing a radio map based on radio fingerprinting measurements. A method comprises: acquiring a radio map of an area, the radio map based on radio frequency measurements performed between at least one access node of a wireless network and terminal devices within the area; detecting at least one gap in the radio map and determining geographical coordinates of the at least one gap; causing transmission of a message comprising at least one information element requesting for additional measurements and comprising the geographical coordinates of the at least one gap; in response to the message, receiving at least one radio frequency measurement report related to at least one terminal device and comprising radio frequency measurement data and at least one measurement location where the radio frequency measurement data has been measured; and updating the radio map on the basis of the radio frequency measurement data.

Abstract: A terminal configured to: receive a monitoring configuration comprising an uplink transmission type identifier; perform monitoring, in accordance with the monitoring configuration, to determine a failure rate of the uplink transmission type; and transmit a monitoring result dependent upon the performed monitoring.

Abstract: There is provided a network apparatus that is caused to receive as part of a handover procedure for handover of a terminal to the network apparatus, metadata about at least one machine learning model accessible for execution and/or training by the terminal, determining whether or not the terminal should execute and/or train the at least one machine learning model after the terminal is handed over to the network apparatus; and signal the result of the determining to the terminal.

Abstract: It is provided a method comprising: checking whether a terminal indicates to a network its capability to execute and/or to train a machine learning model; monitoring whether the terminal is in an inability state; informing the network that the terminal is in the inability state if the terminal indicated the capability and the terminal is in the inability state, wherein, in the inability state, the terminal is not able to execute and/or train the machine learning model, or the terminal is not able to execute and/or train the machine learning model at least with a predefined performance.

Abstract: It is provided an apparatus comprising: one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: monitor if a trained model is available at a terminal; inform a network that the trained model is available at the terminal if the trained model is available at the terminal.

Abstract: A method, apparatus, and a computer-readable storage medium are provided for random access reporting in mixed network types. In an example implementation, the method may include detecting, by a user equipment, random access to at least one or more public and/or one or more non-public networks; and generating, by the user equipment, one or more random access reports, the one or more random access reports including network identifying information related to random access failures associated with the at least one or more public and/or one or more non-public networks.

Abstract: As an aspect, there is provided an apparatus, caused at least to: receive, by a user device from an access node, a measurement configuration for training of a machine learning operations control model, the measurement configuration comprising an indication of at least one parameter to be measured and at least one acceptance condition for measurement values; obtain at least one measurement value; compare the at least one measurement value to the at least one acceptance condition for defining usability of the at least one measurement value for the training, and when the at least one measurement value is usable for the training, carry out the training of the operations control model, and when the at least one measurement value is not usable for the training, transmit the at least one measurement value to the access node.

Abstract: According to an example embodiment, an apparatus includes at least one processor; and at least one memory including computer program code; at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: receive a plurality of channel state information (CSI) values reported by the user device; determine a plurality of estimated reference signal received power (RSRP) values that have been estimated based at least on the plurality of CSI values; and output a sequence of RSRP values, including at least the estimated RSRP values.