Abstract: Methods are provided for managing a wireless device that is operable to connect to a communication network comprising a Radio Access Network (RAN). One method, performed by a node of the communication network, comprises: selecting, on the basis at least of information about one or more of a behavior of the wireless device and a radio environment experienced by the wireless device, a machine-learning (ML) model from a plurality of candidate ML models to be downloaded to the wireless device, the candidate models providing outputs on the basis of which respective RAN operations performed by the wireless device are configured; and causing transmission of the selected ML model to the wireless device for execution by the wireless device.

Abstract: A network node (1700) configured to operate as a source network node (32) of a reconfiguration procedure transmits signaling to a target network node (34) of the reconfiguration procedure. The signaling indicates (i) whether or not the target network node (34) is to provide feedback information (44) to the source network node (32) and/or (ii) which one or more types of feedback information (44) the target network node (34) is to provide to the source network node (32). Based on the received feedback information (44), the network node may train a model to predict predicted information, predict the predicted information using the model, and make a decision based on the predicted information.

Abstract: Systems and methods related to traffic flow prediction in a wireless network are disclosed. In one embodiment, a computer-implemented method comprises collecting training data comprising Internet Protocol (IP) addresses extracted from packets for traffic flows in a wireless network and one or more actual traffic type related parameters for each of the traffic flows. The method further comprises training heavy-hitter IP address encodings based on the extracted IP addresses and encoding the extracted IP addresses using the trained heavy-hitter IP address encodings. The method further comprises training a traffic type predictor of a traffic flow predictor based on the encoded IP addresses and the one or more actual traffic type related parameters for each of the traffic flows, where the traffic type predictor is a learning model that maps encoded IP addresses to one or more predicted traffic type related parameters.

Abstract: A computer implemented method for detecting data (y) comprised in a part (x) of a received signal (w) of a communication system (100), wherein the received signal (w) is associated with a population and where the part (x) of the received signal (w) is associated with a sub-population of the population, the method comprising: configuring (S1) a first function (ƒ1) to determine a context (c) of the received signal (w), wherein the context (c) is indicative of a state of the received signal (w), configuring (S2) a second function (ƒ2) to detect the data (y) based on the part (x) of the received signal, wherein the second function (ƒ2) is arranged to be parameterized by the context (c), and detecting (S3) the data (y) by the first (ƒ1) and second (ƒ2) functions.

Abstract: Methods and systems for reducing data sharing overhead based on similarities of datasets. In one aspect, there is a computer implemented method (1200) for reducing the amount of data transmitted to a network node (102) of a wireless communication system (100). The network node is configured to use the data to create or modify a model. The method is performed by the network node and comprises obtaining (s1202) information identifying a set of available datasets (152-158) and obtaining (s1204) a set of similarity scores. The method also comprises based on the obtained similarity scores, selecting (s1206) a subset of the set of available sets. The method further comprises transmitting (s1208) a request for each dataset included in the subset, receiving (s1210) the requested datasets, and using (s1212) the received datasets to construct or modify a model.

Abstract: Methods and nodes in a communications network for using a machine learning model that has been trained using training data from a first radio environment, in a second radio environment, wherein the machine learning model takes a first set of features as input and outputs a label based on a first labelling scheme, the first labelling scheme having been determined for the first radio environment. A method comprises obtaining (402) a dataset in the second radio environment comprising a plurality of data points comprising values of the first set of features, labelling (404) each data point in the dataset using the first labelling scheme, and determining (406) from the labelled dataset whether the first labelling scheme is suitable for use in the second radio environment.

Abstract: A network node (16) obtains, for each of one or more wireless devices, radio performance information (18) indicating measured radio performances that multiple cells respectively provided to the wireless device (20A) while the wireless device (20A) was simultaneously connected to the multiple cells in multi-connectivity operation under certain conditions. Based on the obtained radio performance information (18), the network node (16) builds a prediction model (24) which models radio performance that a cell will provide under one or more conditions. The network node (16) then makes or configures another network node to make decisions on mobility, scheduling and/or radio configuration of wireless devices based on the prediction model (24).

Abstract: A method is disclosed for managing a wireless device that is operable to connect to a communication network. The communication network comprises a Radio Access Network (RAN), and the method is performed by a RAN node of the communication network. The method comprises receiving, from the wireless device, information indicating whether the wireless device is capable of executing a Machine Learning (ML) model that is operable to provide an output on the basis of which at least one RAN operation performed by the wireless device may be configured.

Abstract: A method in a first node of transferring data from a second node to a first node comprises receiving a message from the second node indicating that the second node has data to transfer to the first node. The method then comprises predicting a possible content of the data and sending the prediction of the possible content of the data to the second node. The method then comprises determining the data based on a response of the second node to receiving the prediction.

Abstract: A method performed by a wireless device (12) comprises receiving (500), from a network node (18) in a wireless communication network (10), signaling (20) that indicates one or more report triggering measurement objects (22) and one or more report content measurement objects (24). The one or more report triggering measurement objects (22) are one or more objects of measurements whose results (22R) are to be evaluated by the wireless device (12) for determining whether to log or send a measurement report (16). The one or more report content measurement objects (24) are one or more objects of measurements whose results (22R) are to be reported by the measurement report (16). The signaling (20) is configurable to indicate at least one report content measurement object (24) that is different from each of the one or more report triggering measurement objects (22) indicated by the signaling (20).

Abstract: A computer implemented method in a communications network for determining location information about an actual location of a drone comprises obtaining (302) a reported location of the drone at a first time point and obtaining (304) a measurement of radio conditions between the drone and a node in the telecommunications network, at the first time point. The method then comprises predicting (306) radio conditions at one or more locations related to the reported location of the drone, and determining (308) the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions.

Abstract: Embodiments include methods for a network node to configure random access by one or more user equipment, UEs, in a cell of the wireless network. Such methods include providing (2040) one of the following to one or more UEs operating in the cell: an artificial intelligence/machine learning, AI/ML, predictive model that includes one or more input parameters and corresponding one or more output parameters that are associated with random-access configurations for the cell; or one or more random-access configurations for the cell, each random-access configuration associated with one or more values of output parameters of the AI/ML predictive model. Such methods include detecting (2090) a random access to the cell, by a particular UE, according to a particular random-access configuration associated with particular values of the output parameters. Other embodiments include complementary methods for a UE, as well as network nodes and UEs configured to perform such methods.

Abstract: In a first method, a wireless device estimates a delay profile of a channel impulse response, CIR, for a channel between a network node and the wireless device, compresses the delay profile using a compression function, and transmits the compressed delay profile. The compression function includes a first function and a quantizer. The first function is configured to receive input data and reduce a dimension of the input data. In a second method, a network node receives a compressed delay profile of CIR for a channel between a network node and a wireless device, decompresses the compressed delay profile using a decompression function, and estimates a position of the wireless device based on at least the decompressed delay profile. The decompression function includes a first function which is configured to receive input data and provide output data in a higher dimensional space than the input data.

Abstract: Embodiments include methods for managed machine learning (ML) in a communication network, such as by one or more first network functions (NFs) of the communication network. Such methods include determining whether processing of an ML model in the communication network should be distributed to one or more user equipment (UEs) operating in the communication network, based on characteristics of the respective UEs. Such methods also include, based on determining that the processing of the ML model should be distributed to the one or more UEs, establishing trusted execution environments (TEEs) in the respective UEs and distributing the ML model for processing in the respective TEEs. Other embodiments include complementary methods for UEs, as well as UEs and NFs (or communication networks) configured to perform such methods.

Abstract: Systems and methods for providing unlicensed drone User Equipment (UE) detection in a cellular communications network are disclosed. In this regard, embodiments of a method of operation of a server for providing unlicensed drone UE detection in a cellular communications network are disclosed. In some embodiments, the method includes receiving, from a network node, a measurement report for a UE and predicting that the UE is an unlicensed drone UE based on the measurement report for the UE. The method further includes taking one or more actions upon predicting that the UE is an unlicensed drone UE. In this manner, an efficient unlicensed drone detection mechanism is provided.

Abstract: A computer implemented method is disclosed for estimating a position of a vehicle. The method comprises obtaining dynamic state information for the vehicle at a first time in a time sequence the dynamic state information comprising position information for the vehicle. The method further comprises receiving, from the vehicle, communication network information for the vehicle at a second time in the time sequence that is after the first time, wherein the communication network information comprises a result of a measurement carried out by the vehicle on a signal exchanged with the communication network. The method further comprises using a trained ML model to estimate a position of the vehicle at the second time in the time sequence on the basis of the obtained dynamic state information and received communication network information. Also disclosed are a communication network node, a training agent and associated methods.

Abstract: It is provided a method for detecting when a user equipment, UE, is airborne. The method is performed in a UE status detector and comprises the steps of: obtaining an indicator of variation of signal strengths for signals received in the UE, wherein the signals are transmitted for at least three different cells; and determining, based on the indicator of variation, when the UE is airborne.

Abstract: Systems and methods for configuring and generating Narrowband Positioning Reference Signals (NPRS) for NB-IoT are provided. A network node provides (NPRS) configuration information indicating a system frame number (SFN) dependent resource mapping. A wireless device obtains a NPRS in accordance with the indicated SFN dependent resource mapping and uses it to perform a radio measurement operation.

Abstract: A method is performed by a wireless device. The method includes receiving, from a network, an indication for the wireless device to prepare for a change from a first ground station to a second ground station. Each of the first ground station and the second ground station is configured to communicate with the wireless device via one or more satellites. The method further includes preparing to change from the first ground station to the second ground station. The method further includes communicating with the second ground station via the one or more satellites.

Abstract: In a method in a user equipment, UE, in a communications network, of determining whether to perform a handover procedure from a first network node to a second network node, a location of the UE is provided as input to a model stored on the UE, the model having been trained using a machine learning process to predict conditions on the second network node in the communications network based on the location of the UE. A prediction of conditions on the second network node at the provided location of the UE is provided by the model. The received predicted conditions are then used to determine whether to perform a handover procedure.