Methods and nodes in a communications network for training an autoencoder

A computer implemented method in a second 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 comprising:

• receiving from a first node a baseline version of a first component model on CSI data available at the first node;
• training a second component model to perform a complementary encoding operation with respect to the baseline version of the first component model, using CSI data available at the second node;
• and
• sending a first data product based on the training to the first node, for use by the first node in further training of the first component model.

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 first node comprising:

• a memory comprising instruction data representing a set of instructions;
• and
• a processor configured to communicate with the memory and to execute the set of instructions, wherein the first node is configured to:
• i) train 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;
• and
• ii) initiate further training of the first component model using a second data product from the third node, wherein the further training comprises freezing a second subset of horizontal layers in the first component model during a second backward pass training stage, the first subset of horizontal layers being different to the second subset of horizontal layers.

A second 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 second node comprising:

• a receiver operable to receive from a first node a baseline version of a first component model on CSI data available at the first node;
• a memory comprising instruction data representing a set of instructions;
• and
• a processor configured to communicate with the memory and to execute the set of instructions, wherein the second node is configured to:
• train a second component model to perform a complementary encoding operation with respect to the baseline version of the first component model, using CSI data available at the second node;
• and
• send a first data product based on the training to the first node, for use by the first node in further training of the first component model.