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.

Abstract: The present subject matter relates to an apparatus for handover of a user equipment into a target base station of communication system, the apparatus comprising means, the means being configured for: in response to selecting the target base station for handover of the user equipment to the target base station performing a first handover method by at least: comparing a current set of cell measurements of the user equipment with reference sets of cell measurements for determining whether the target base station is a fake base station; performing a handover of the user equipment based on a determination that the target base station is not a fake base station.

Abstract: There is herein disclosed a network node of a wireless communication system. The network node has means for selecting a first group of cells. Each cell of the first group of cells is a secondary cell, SCell, for carrier aggregation, CA, on a user equipment, UE, of the wireless communication system. The network node has means for obtaining a first set of labelled data for the first group of cells. The labelled data includes performance information regarding the first group of cells. The network node has means for training a machine learning model for the first group of cells using the first set of labelled data. The machine learning model is configured to be implemented on the network node of the wireless communication system for SCell selection. The network node has means for determining whether the machine learning model meets a performance criteria for at least one cell of the first group of cells.

Abstract: There is herein disclosed a network node of a wireless communication system. The network node has means for selecting a first group of cells. Each cell of the first group of cells is a secondary cell, SCell, for carrier aggregation, CA, on a user equipment, UE, of the wireless communication system. The network node has means for obtaining a first set of labelled data for the first group of cells. The labelled data includes performance information regarding the first group of cells. The network node has means for training a machine learning model for the first group of cells using the first set of labelled data. The machine learning model is configured to be implemented on the network node of the wireless communication system for SCell selection. The network node has means for determining whether the machine learning model meets a performance criteria for at least one cell of the first group of cells.

Abstract: An apparatus including: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive, from a plurality of network nodes, information related to at least one parameter of models of the plurality of network nodes; determine at least one cluster of the plurality of network nodes based on at least one similarity criterion and the information related to the least one parameter of models of the plurality of network nodes; and determine at least one global model for the at least one cluster using local models of network nodes that belong to the at least one cluster.

Abstract: Test equipment (TE) may, while in a test configuration, cause transmission of a first reference signal for receipt by a communication device under test (DUT). The TE may receive a first line-of-sight indicator value as determined by the DUT in accordance with a method having a known accuracy. While in the test configuration, the TE may cause transmission of a second reference signal for receipt by the DUT. The TE may receive a second line-of-sight indicator value as determined by the DUT based at least in part on a trained machine learning (ML) model. The TE may determine an error value between the first line-of-sight indicator value and the second line-of-sight indicator value. Based at least in part on the error value, the TE may determine whether the trained ML model passes a conformance test related to estimation of line-of-sight indicator values by the trained ML model.

Abstract: A first base station and a central entity for use in a radio access network implementing a dual/multiconnectivity scheme where the first base station is caused to split downlink data associated with a plurality of user devices between a first path through the first base station and a second path through the at least one second base station. The splitting comprises a plurality of reinforcement learning agents associated with the plurality of user devices. The reinforcement learning agents apply a current common neural network policy to select a path amongst the first and the second paths based on observed performance metrics of the first and second paths and to generate a reward associated with the selected path and send their experiences to the central entity. The central entity updates the common neural network policy based on the received experiences. The central unit can be implemented in a RIC.

Abstract: An apparatus comprising at least one processor, and at least one memory. The at least one memory stores instructions that, when executed by the at least one processor, caused the apparatus to train a neural network configured to be used to infer an effective isotropic radiated power for at least one angle and at least one weight, and to obtain, based on the training, a trained neural network which is used for inference of the effective isotropic radiated power for the at least one angle and the at least one weight. The trained neural network is transmitted to at least one distributed unit.

Abstract: A RL agent performs a RL process to configure at least one Discontinuous Reception, DRX, cycle for a User Equipment, UE. An action is selected by the RL agent in an action space. Each action in the action space corresponds to a DRX cycle configuration. The RL agent sends to the UE indication to use the DRX cycle configuration corresponding to the selected action. The RL agent receives state information computed over at least one DRX cycle configured based on a DRX cycle configuration indicated by the RL agent. The RL agent computes a reward on the basis of the state information.

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: Example embodiments of the present disclosure are related to artificial intelligence/machine learning (AI/ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI/ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model. The first apparatus determines a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated.

Abstract: The present disclosure relates to a reinforcement learning (RL) on beam management. In particular, it utilizes side links capabilities for enabling real data/traffic exchange for RL explorative training step. In this way, it enables a radio system performance friendly RL learning training operation from one side and will utilize the available radio air resources on the other side.

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for a model monitoring for positioning, especially for assisted Artificial Intelligence/Machine Learning (AI/ML) positioning without measured ground truth (GT). The method comprises: determining, based on three or more transmission reception points, TRPs, at least one reference location associated with a positioning performance monitoring of a second apparatus; generating assistance data for monitoring a positioning performance at the second apparatus at least comprising at least one of: respective positioning measurement data of at least one positioning measurement type in the at least one reference location; respective monitoring metric associated with the at least one positioning measurement type in the at least one reference location; or the at least one reference location; and transmitting the assistance data to a second apparatus.

Abstract: Systems, methods, apparatuses, and computer program products for verifying positioning accuracy. One method may include transmitting, by testing equipment, a request to measure location coordinates to a device, receiving, by the testing equipment, from the device, a model inference comprising at least one location coordinate, and determining, by the testing equipment, whether a difference between the model inference and at least one ground truth is within a threshold value for a defined number of samples, wherein the at least one ground truth comprises at least one known location coordinate of a test point.

Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium of noisy positioning data processing. In a method, a first apparatus obtains measurement data and first positioning data associated with the measurement data. The first apparatus generates second positioning data from the first positioning data based on network assistance information. The first apparatus transmits at least the measurement data and the second positioning data. In this way, an accuracy of the positioning data can be improved.

Abstract: Solutions for adaptive performance monitoring are disclosed. A solution comprises maintaining (200) ability to collect network performance data utilising a collecting policy from more than one collecting policy, receiving (202) from a network element a request to apply a given collecting policy, applying (204) the requested collecting policy in collecting network performance data and transmitting (206) network performance data to network based on the applied policy.

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: To provide fairness between served apparatuses while keeping radio frequency exposure below a defined limit, an apparatus collects, per a sampling period, historical data on tokens at the sampling period, the historical data including at least number of tokens requested during the sampling period, wherein a token is indicative of an amount of radiated power for transmission of a data element. The collected historical data is transmitted to a wireless network. The wireless network determines hindsight based estimations for maximum token consumptions that should have been allowed, and then determines for a control policy, using at least numbers of tokens requested in the historical data received and corresponding hindsight based estimations determined, updated weight values, and transmit them to the apparatus. The apparatus then updates its control policy correspondingly, applies it, collects historical data, and transmits it to the wireless network to obtain updated weight values.

Abstract: A method includes receiving, by a user device, a machine learning model policy associated with model update, wherein the machine learning model policy comprises at least one of the following: difference information for a machine learning model with respect to model structure or model configuration parameters; difference information with respect to one or more weights or biases of the machine learning model; or difference information with respect to one or more layers of the machine learning model; carrying out updating of the machine learning model according to the machine learning model policy; and transmitting, by the user device to a network node, information on at least one change to the machine learning model caused by the updating, for reducing overhead with respect to transmitting a full updated version of the machine learning model.

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.