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: A method may include receiving, by a first user device from a network node or a second user device, 1) a positioning measurement report including at least one positioning measurement measured by a reference device, and 2) reference positioning-related information to be used for testing and/or validating a machine learning model; determining estimated positioning-related information as outputs of the machine learning model based on at least a portion of the positioning measurement report as inputs to the machine learning model; determining a performance indication of the machine learning model based on the reference positioning-related information and the estimated positioning-related information, wherein the performance indication indicates a performance or accuracy of the machine learning model; and performing, by the first user device, an action based on the performance indication.

Abstract: In a communication network, a device is configured to predict attacks and detect attacks from data logs received from the network and generate a response to an attack upon prediction or detection of an attack. Graph representations of data logs are generated based on a predefined schema. Attacks are detected by applying inference rules to a graph representation of the data logs. Attacks are predicted by using a graph neural network trained with subgraphs obtained by querying a graph representation of training data corresponding to normal traffic and attacks.

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 method is provided for receiving, at a data collection entity, from each cell, a time series of a respective set of data where each set of data includes at least: one or more per-cell performance measurement data, one or more per-cell serving beams of each scheduled UE device, and a time and frequency allocation of each scheduled UE device to be served by the corresponding one or more per-cell serving beams; generating, by a cSON entity, from the sets of data received from the data collection entity, a set of cross-beam inter-cell interference profiles; establishing, by the cSON entity, from at least the set of cross-beam inter-cell interference profiles, a beam scheduling policy; receiving, at each cell, from the cSON entity, the beam scheduling policy; and applying, by a respective scheduler at each cell, the beam scheduling policy to each of the one or more per-cell serving beams.

Abstract: A method for a physical random access channel (PRACH) attack detection includes detecting by a base station a plurality of preambles sent by devices through a PRACH; launching by the base station a random access (RA) procedure for at least one device for which a preamble has been detected; decoding by the base station at least one radio resource control (RRC) connection request received in the context of a launched RA procedure; determining by the base station a first metric and a second metric, wherein the first metric is the number of RRC connection requests successfully decoded over time, wherein the second metric is the number of preambles detected over the same time and for which a RA procedure has been launched; determining whether there is a suspicion of storm attack over the PRACH based on a result of a comparison of the first and second metrics.

Abstract: An apparatus comprises means for performing, at a CU of a master node: receiving an individual PDU from one or more incoming PDUs, wherein each PDU is to be transmitted to a UE via one amongst respective paths towards at least one DU of the master node and at least one secondary node, the UE being in DC or MC with the master node and the at least one secondary node; collecting a respective data set for each of the paths; receiving, from a ML model inputting each data set, a respective estimated delay of transmission for each of the paths; selecting, from each of the paths, a path based on the estimated delay of transmission; and transmitting the PDU to the UE via the selected path.

Abstract: Methods and apparatus are proposed for computing a power reduction policy to control radio emissions of a base station over a time window, with the objective to keep the time-averaged radio emissions over the time window below an authorized value set by regulations, with minimum impact on the performance of the overall system. The time window is subdivided into a plurality of time periods starting at a plurality of time steps, and the policy control comprises, at least at a first time step in the time window: generating a predicted average radio emissions over subsequent time periods in the time window, based on radio emissions historical data; finding couples of values of a power reduction factor and an average radio emissions that minimize a cost function for each time step in the time window, based on the predicted average radio emissions.

Abstract: A deep transfer reinforcement learning (DTRL) method based on transfer learning within a deep reinforcement learning (DRL) framework is provided to accelerate the GoB optimization decisions when experiencing environment changes in the same source radio network agent or when being applied from a source radio network agent to a target radio network agent. The transferability of the knowledge embedded in a pre-trained neural network model as a Q-approximator is exploited, and a mechanism to transfer parameters from a source agent to a target agent is provided, where the transferability criterion is based on the similarity measure between the source and target domain.

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.

Abstract: A method and apparatus (100) for beam failure management for a user equipment (UE) (104) are described. A beam failure instance counter (BFI_counter) is determined, that is indicative of a number of consecutive beam failure instances occurred at the UE (104), at a time instance. A location of the UE (104) is determined at the time instance. A beam failure probability factor is determined, based at least on the location of the UE (104) at the time instance and the BFI_counter. The beam failure probability factor is indicative of a probability of occurrence of a beam failure at the location of the UE (104), after a plurality of further time instances. Further, the beam failure probability factor is compared with a beam failure threshold probability (beamF ailureThresholdProb). Thereafter, a beam failure is declared if the beam failure probability factor is higher than the beamFailureThresholdProb.

Abstract: Disclosed is a method comprising obtaining a plurality of handover parameter values, using a first machine learning model to select a subset of handover parameter values from the plurality of handover parameter values, obtaining historical information of a plurality of terminal devices, determining a first set of optimal handover parameter values for the plurality of terminal devices from the subset of handover parameter values, tagging the first set of optimal handover parameter values with the historical information of the plurality of terminal devices to obtain a labelled dataset, and training a second machine learning model with the labelled dataset, wherein the trained second machine learning model is capable of predicting a second set of optimal handover parameter values for a first terminal device based on historical information of the first terminal device.

Abstract: A method comprising receiving a first indication, from a terminal device, indicating that the terminal device is capable of supporting carrier aggregation, obtaining information regarding a battery level of the terminal device, estimating battery consumption per one carrier component, and based, at least partly, on the estimated battery consumption and information regarding the battery level, determining a number of additional carrier components to be activated for the terminal device.

Abstract: An apparatus, method and computer program is described including: obtaining first channel measurement data for a primary component carrier used for communications between a device and a network node of a mobile communication system; providing the first channel measurement data as an input to an algorithm to obtain a first embedding output; comparing the first embedding output with embedding outputs of a plurality of reference channel measurement data input to said algorithm to identify a closest reference channel measurement data to the first channel measurement data; identifying a set of reference channel information associated with the identified closest reference channel measurement data, wherein the identified set of reference channel information includes a carrier aggregation policy defining one or more secondary communication channels; and setting a carrier aggregation policy for the primary component carrier in accordance with the carrier aggregation policy.

Abstract: Disclosed is a method comprising obtaining historical data from a plurality of access nodes comprised in a network, determining, for at least one of the plurality of access nodes, a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, determining, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off, providing the threshold pair to the network for deployment, and collecting data regarding at least one key performance indicator from the plurality of access nodes.

Abstract: Disclosed is a method comprising obtaining historical data from a plurality of access nodes comprised in a network, determining, for at least one of the plurality of access nodes, a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, determining, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off, providing the threshold pair to the network for deployment, and collecting data regarding at least one key performance indicator from the plurality of access nodes.

Abstract: An apparatus for anomaly detection in a network, using an autoencoder including an encoder and a decoder.

Abstract: Systems, methods, apparatuses, and computer program products for determining a grid-of-beams (GoB) are provided. One method may include collecting network data for training a neural network, train the neural network, using the collected data, to learn a non-discounted cumulative reward Q that evaluates a benefit of including a given beam into a grid-of-beams (GoB), iteratively applying the trained neural network to select at least one optimal beam to include in the grid-of-beams (GoB), and selecting one or more beams from the grid-of-beams (GoB) to transmit to a user equipment or to receive transmission from the user equipment.

Abstract: A method is provided for determining, at a first stage, a first position of a communications device, inputting, from the first stage, the first position to a machine learning model at a second stage, and determining, at the second stage, at least based on the first position from the first stage, a second position of the communications device by using the machine learning model. The first position is determined at the first stage by using one of at least a non-machine learning model and a machine learning model.

Abstract: An apparatus, method and computer program are disclosed. The apparatus may include circuitry configured for receiving from a target device, at a first time instance, a set of first measurement data associated with each of a plurality of base stations and determining a first position of the target device based on the received first sets of measurement data. The circuitry may also receive from the target device, at each of one or more subsequent time instances, a second set of measurement data associated with one, or each of a smaller number, of the base stations and determining, at each of the one or more subsequent time instances, a respective position of the target device based on the position determined at a previous time instance and the second set of measurement data.