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: 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: 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 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: 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: 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 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.

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: 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 (16) 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: 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: According to an aspect, there is provided an apparatus for performing the following. The apparatus performs cell-specific radio measurements for a plurality of cells or beam-specific radio measurements for a plurality of transmit beams. The apparatus evaluates correlation between results of the cell/beam-specific radio measurements and compares the correlation against one or more pre-defined correlation conditions. In response to the correlation for at least one pair of cells in the plurality of cells or for at least one pair of transmit beams in the plurality of transmit beams satisfying the one or more pre-defined correlation conditions, the apparatus filters the results of the cell-specific or beam-specific radio measurements to remove repetitions of results satisfying the one or more pre-defined correlation conditions and transmits, to another apparatus, a measurement report comprising the filtered results and information on the correlation.

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: Devices, methods and computer programs for determining a radio transmission power threshold are disclosed. At least some of the example embodiments described herein may allow improving resource usage by serving a maximum amount of traffic and not wasting resources, while also ensuring the possibility of serving a minimum traffic level.

Abstract: Apparatuses, methods and computer programs are described including, receiving user equipment downlink power measurements from one or more base stations of a communication system, wherein the one or more base stations are configured to provide a plurality of user equipment downlink power measurements for user equipment in communication with a respective base station of the one or more base stations; determining inter-beam interference for 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: Devices, methods and computer programs for determining a radio transmission power threshold are disclosed. At least some of the example embodiments described herein may allow improving resource usage by serving a maximum amount of traffic and not wasting resources, while also ensuring the possibility of serving a minimum traffic level.

Abstract: A method comprising: determining at a first device a requirement for a spatial distribution associated with training samples for a machine learning model, wherein the machine learning model is used in solving a positioning task; performing at least one of: transmitting, to the second device, information indicating the requirement for training the machine learning model, or training the machine learning model by using a training dataset comprising training samples satisfying the requirement.

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: Various techniques are provided for a method including communicating, by a user equipment (UE) to a network device, a message including a measurement relaxation request, receiving, by the UE from the network device, a message including one of a measurement relaxation approval or a measurement relaxation denial, in response to receiving the measurement relaxation approval predicting, by the UE, a measurement relaxation configuration using a machine learning model, communicating, by the UE to the network device, a message including the measurement relaxation configuration, receiving, by the UE from the network device, a message including a measurement relaxation acknowledgement, and reporting, by the UE to the network device, measurements based on the measurement relaxation configuration.