Abstract: There are provided measures for enablement of federated machine learning for terminals to improve their machine learning capabilities. Such measures exemplarily comprise, at a terminal, receiving a configuration indicative of an instruction to participate in federated learning of a global machine learning model, the configuration including timing information related to said federated learning, and performing, based on said configuration, a machine learning process based on undertaken network performance related measurements, wherein said timing information includes a time limit with respect to a local machine learning model resulting from said machine learning process, and said time limit is a specification of a moment in time by when said local machine learning model is to be completed or a specification of a moment in time by when transmission of said local machine learning model is to be completed, and wherein said configuration is a minimization of drive tests configuration.

Abstract: Various techniques are provided for receiving, by a base station (BS) from a user equipment (UE), a communication including a feature vector, storing, by the BS, a dataset including one or more feature vectors associated with the UE, communicating, by the BS to a network device, the dataset associated with the UE, receiving, by the BS from the network device, a machine learning (ML) model, the ML model being trained, using the dataset, to detect UE orientation, and communicating, by the BS to the UE, the trained ML model.

Abstract: The present disclosure relates to a technical solution that improves the reliability of communications over a wireless communication channel by replacing a conventional interleaver (e.g., random interleaver) with a Neural Network (NN)-generated interleaver. For this purpose, a well-trained NN is used, which is configured to receive a UE connection status and a channel status as input data and outputs the interleaver in the form of an orthogonal binary matrix. The NN is shared by a UE and a network node. The network node may use the interleaver to interleave a set of bits in a downlink control message before the downlink control message is encoded, e.g., with an error correcting code, such as a polar code. The UE may generate and transpose the interleaver to obtain a deinterleaver to be applied to the downlink control message after its decoding (e.g., polar decoding).

Abstract: Disclosed is a method comprising selecting (401), by a base station, a power amplifier distortion model from a set of power amplifier distortion models, wherein the power amplifier distortion model comprises a pre-trained machine learning model configured to compensate power amplifier distortion. The method further comprises receiving (402), by the base station, one or more uplink data transmissions from a terminal device, and compensating (403), by the base station, at least a part of the power amplifier distortion from the one or more uplink data transmissions based at least partly on the power amplifier distortion model.

Abstract: Various techniques are provided for receiving, by a base station (BS) from a user equipment (UE), a communication including a feature vector, storing, by the BS, a dataset including one or more feature vectors associated with the UE, communicating, by the BS to a network device, the dataset associated with the UE, receiving, by the BS from the network device, a machine learning (ML) model, the ML model being trained, using the dataset, to detect UE orientation, and communicating, by the BS to the UE, the trained ML model.

Abstract: There are provided measures for enablement of federated machine learning for terminals to improve their machine learning capabilities. Such measures exemplarily comprise, at a terminal, receiving a configuration indicative of an instruction to participate in federated learning of a global machine learning model, the configuration including timing information related to said federated learning, and performing, based on said configuration, a machine learning process based on undertaken network performance related measurements, wherein said timing information includes a time limit with respect to a local machine learning model resulting from said machine learning process, and said time limit is a specification of a moment in time by when said local machine learning model is to be completed or a specification of a moment in time by when transmission of said local machine learning model is to be completed, and wherein said configuration is a minimization of drive tests configuration.