Abstract: A method comprises receiving from a user equipment supporting Carrier Aggregation, CA, Channel State Information, CSI, reports for a Primary Component Carrier, PCC, for one or more previous time slots and for a current time slot. CSI reports for a Secondary Component Carrier, SCC are received from the user equipment. The CSI reports include a CSI report for a previous time slot. Each CSI report includes a value of a channel state indicator. The method further comprises predicting a value of the channel state indicator for the SCC for the current time slot; the value is predicted based upon one or more values of the channel state indicator reported for the PCC for the one or more previous time slots and for the current time slot, and at least one value of the channel state indicator reported for the SCC for the previous time slot.

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: First user equipments are detected out of a plurality of user equipments of a cellular communication system (S201). The user equipments respectively correspond to a distributed node of a federated machine-learning concept and respectively generate a partial machine-learning model, wherein partial machine-learning models generated by the plurality of user equipments are to be used to update a global machine-learning model at the network side of the cellular communication system. The first user equipments are user equipments comprising ready partial machine-learning models.

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

Abstract: The invention relates to a method for configuring carrier aggregation for a current user equipment (14) located in a wireless communication network in a coverage area of a base station which is capable of emitting a plurality of carrier components with a plurality transmission beams per carrier component, the method comprising: retrieving quality metrics beams from a database comprising quality metrics associated with a carrier component of the plurality of carrier components and with a user equipment of a plurality of user equipments; determining, using the quality metrics, one or more candidate carrier components to be configured or de-configured for the current user equipment as secondary carrier components; configuring or de-configuring said one or more carrier components.

Abstract: Apparatuses and methods in a communication system are disclosed. Data on error between the location of more than one user terminal and the estimated location of the more than one user terminal is obtained, the estimated location obtained utilising radio frequency fingerprinting. It is evaluated whether the error is greater than a given threshold, and if so it is determined whether the error is due to radio frequency fingerprinting or not. Based on the determination decision is made on initialising update of radio frequency fingerprinting map.

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

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 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.