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: 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: Disclosed is a method for determining open loop power control parameters. A first set of key performance indicators associated with a first set of open loop power control parameters is obtained from one or more first base stations. A statistical model is determined based at least partly on the first set of key performance indicators and the first set of open loop power control parameters. A second set of open loop power control parameters is determined based at least partly on the statistical model.

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 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 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: A method comprising: receiving a request for a location of a communication device with information indicating an associated required location quality of service of said location; and in response to said information, determining which of a plurality of location methods to use and one or more parameters for said determined location method.

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: Disclosed is a method for determining open loop power control parameters. A first set of key performance indicators associated with a first set of open loop power control parameters is obtained from one or more first base stations. A statistical model is determined based at least partly on the first set of key performance indicators and the first set of open loop power control parameters. A second set of open loop power control parameters is determined based at least partly on the statistical model.

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 and method is disclosed in relation to antenna arrays. The method may include transmitting a reference signal to one or more remote user terminals from each of a plurality of sub-arrays of an antenna array, each sub-array including a quantity of radiating elements of the antenna array capable of establishing a backhaul link with a remote communications node. The method may also include receiving measurement signals from the one or more remote user terminals, indicative of one or more characteristics of the received reference signals. The method may also include selecting, based on received measurement signals, a first subset of the sub-arrays for backhaul communications with the remote communications node and a second subset of the sub-arrays, including one or more of the remaining sub-arrays, for access communications with the one or more remote user terminals.

Abstract: Apparatuses, methods and computer programs are described comprising: receiving user equipment downlink power measurements from a one or more base stations of a communication system, wherein the or each base station provides a plurality of user equipment downlink power measurements for user equipment in communication with said base station; determining inter-beam interference for measurements multiple directive beams of the or each base station based on the user equipment download power measurements; and assigning one or more beams to one or more clusters such that, for all beams within a cluster, inter-beam interference is below a threshold, wherein beams within a cluster share time and/or frequency resources.

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.