Abstract: A computer-implemented method in a first node in a communications network for training a first component model of an Autoencoder, AE, machine learning model, the first component model being either an encoder or a decoder and wherein the first component model is for use in exchanging compressed Channel State Information (CSI) between the first node, a second node, and a third node in the communications network. The method comprises: i) training (502) the first component model using a first data product obtained from the second node, wherein the training comprises freezing a first subset of horizontal layers in the first component model during a first backward pass training stage.

Abstract: A computer-implemented method performed by a computing device including a NWDAF having a function to identify and modify dependencies between NWDAFs and at least a first NWDAF consumer. The method includes identifying a relationship between at least the first NWDAF consumer from a plurality of NWDAF consumers that respectively subscribe to the NWDAFs that have respective machine learning, ML, models that have an overlap between a first input feature and respectively have different outputs. At least the first NWDAF consumer uses a first output from the NWDAFs to output an action from the first NWDAF consumer. The method further includes determining whether a second input feature to (i) a respective ML model of the NWDAFs or (ii) a new ML model of an unsubscribed NWDAF can make a positive contribution to the prediction of the output of at least the first NWDAF consumer.

Abstract: A method performed by a first network node is provided. The first network node includes a decoder of a first autoencoder. The method comprises receiving first encoded data. The first encoded data was transmitted by a second network node, and the first encoded data was generated using an encoder of a second autoencoder. The encoder of the second autoencoder is included in the second network node. The method also comprises converting the first encoded data into first converted-encoded data, thereby mapping output of the encoder of the second autoencoder to output of the encoder of the first autoencoder; and decoding the first converted-encoded data using the decoder of the first autoencoder. The first autoencoder and the second autoencoder are different.

Abstract: A method (200) for facilitating detection of jamming signals on the Physical layer of a communication network is disclosed. The method is performed by a first node of the communication network and comprises receiving, from each of a plurality of second nodes, a value of at least one parameter characterizing the respective second node (210), and obtaining measurement data of radio signals in a coverage area of the first node (220). The method further comprises determining, from at least the received parameter values, a number of source ML models to be trained to detect jamming signals (230), and training the determined number of source ML models using the obtained measurement data of radio signals in a coverage area of the first node (240).