Abstract: Communication of Measurement Results in Coordinated Multipoint Methods (200, 400, 600) performed by a wireless device (1100), Radio Access node 5 (1300) and training agent (1500) are disclosed. The wireless device is operable to receive signals over a communication channel from a Transmission Point (TP) of a communication network, which TP is a member of a Coordinated Multipoint (CoMP) set and may be managed by the Radio Access node. The method (200) performed by the wireless device comprises performing a measurement on communication channels to 0 each of a plurality of TPs in the CoMP set (210), and using an autoencoder to compress a vector of measurement results of the performed measurements (220). The method further comprises transmitting the compressed vector of measurement results to TPs in the CoMP set (230) and receiving a joint transmission from TPs in the CoMP set (240).

Abstract: The present disclosure generally relates to wireless communication methods and wireless communication networks, more particularly to for example wireless communication networks comprising fully end-to-end Machine Learning based air-interfaces.

Abstract: Methods and apparatuses for noise learning-based denoising of noisy input data Y that is equal to the original data X plus the noise N (i.e., Y=X+N). In contrast with a conventional denoising autoencoder (DAE) method that attempts to learn the original data X directly from noisy input data Y, the noise learning-based denoising learns the noise N in the noisy input data Y and then regenerates the original data X by subtracting the learned noise N from the noisy input data Y. Learning the noise N may include inputting the noisy input data Y into an encoder of a neural network, and the learned noise N may be output from a decoder of the neural network. Training the neural network may include inputting noisy training data into an encoder of the neural network and outputting training noise from a decoder of the neural network.

Abstract: A method for dynamically assigning communication resources between two or more radio access technologies (RAT) in a wireless access network. The method includes obtaining a network observation ot indicating a current state of the wireless access network, predicting a sequence of future states of the wireless access network by iteratively simulating hypothetical communication resource assignments a1, a2, a3 over a time window w starting from the current state, evaluating a reward function for each hypothetical communication resource assignment a1, a2, a3 over the time window w, and dynamically assigning the communication resources based on the simulated hypothetical communication resource assignment a1 associated with maximized reward function over the time window w when the wireless access network is in the current state.

Abstract: A computer implemented method performed by a network node in a non-public communication network is provided. The method includes predicting a subsequent position of a first communication device served by a public communication network, wherein the first communication device is causing an interference level on the non-public communication network. The method further includes estimating a level of subsequent interference from the first communication device for the subsequent position. The method further includes initiating control of radio resources to reduce the estimated interference level from the first communication device.

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: A computer implemented method in a node of a communications network for scheduling radio resources to a user equipment, UE, in order to provide a service to a user of the UE. The method includes obtaining one or more physiological parameters of the user of the UE. The method further includes scheduling resources to the UE, based on the one or more physiological parameters, so as to provide the service to the UE with a predetermined quality of experience, as perceived by the user.

Abstract: The present disclosure relates to a computer implemented method, performed by a wireless device, for reducing data transmission in a communications network by compression of measurements of network characteristics of the communications network, comprising the method steps of: obtaining a trained encoder, of a trained autoencoder, performing a measurements of network characteristics of the communications network, applying the trained encoder to compress the result of the measurements of network characteristics, and transmitting the compressed representation of measurements of network characteristics towards a network entity, whereby the network entity may perform a method comprising the method steps of: obtaining a trained decoder, of the trained autoencoder, receiving a compressed representation of a measurements of network characteristics, and applying the trained decoder to reconstruct the compressed representation of the measurements of network characteristics.

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: 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: In a method in a node of a communications network for scheduling enhanced mobile-broadband, eMBB, transmissions, the eMBB coverage area interferes with an ultra-reliable low latency communication network, URLLC. In a first block (302) a time division duplex, TDD, pattern for the URLLC is predicted based on historical URLLC TDD data. In a second block (304) an eMBB TDD pattern is scheduled, based on the predicted URLLC TDD pattern, so as to manage cross-link interference on the URLLC network.

Abstract: Communication of Measurement Results in Coordinated Multipoint Methods (200, 400, 600) performed by a wireless device (1100), Radio Access node 5 (1300) and training agent (1500) are disclosed. The wireless device is operable to receive signals over a communication channel from a Transmission Point (TP) of a communication network, which TP is a member of a Coordinated Multipoint (CoMP) set and may be managed by the Radio Access node. The method (200) performed by the wireless device comprises performing a measurement on communication channels to 0 each of a plurality of TPs in the CoMP set (210), and using an autoencoder to compress a vector of measurement results of the performed measurements (220). The method further comprises transmitting the compressed vector of measurement results to TPs in the CoMP set (230) and receiving a joint transmission from TPs in the CoMP set (240).

Abstract: A computer implemented method performed by an application programming interface (APIR) for a network node in a radio access network is provided. The APIR for the network node can receive a request from a data processing entity to transition to a defined network operating condition in the radio access network. The APIR for the network node can further transition to the defined network operating condition during a defined time period. The APIR for the network node can further collect data during the defined time period including at least one observation of the defined network operating condition, an action in the radio access network during the defined network operating condition, and a subsequent observation of the radio access network based on the action. A method performed by a data processing entity is also provided.

Abstract: A node in a communications network is configured to use a reinforcement learning agent to manage a process associated with the communications network. The node is further configured to adjust a value of an exploration rate associated with training of the reinforcement learning agent, based on a performance indicator associated with the managed process.