Method and apparatus for sequential learning of two-sided artificial intelligence/machine learning model for feedback of channel state information in communication system

A method of a second training node, comprising:

• receiving, from a first training node, two-sided artificial intelligence/machine learning (AI/ML) training data information including a reduced sequential training data set;
• performing sequential training on a two-sided AI/ML model for channel state information (CSI) feedback using the reduced sequential training data set;
• and
• transmitting CSI feedback information to the first training node based on the two-sided AI/ML model,
• wherein the two-sided AI/ML model includes at least one of an encoder model and a decoder model, the reduced sequential training data set includes multiple sequential training data, each of the multiple sequential training data includes a pair of channel information and mapping information for the channel information, the channel information includes at least one sample, and the channel information is expressed as a channel matrix or a precoding vector.

A first training node comprising at least one processor, wherein the at least one processor causes the first training node to perform:

• performing training on a first two-sided artificial intelligence/machine learning (AI/ML) model using a raw training data set collected for channel state information (CSI) feedback;
• generating a sequential training data set for sequential training on the first two-sided AI/ML model;
• and
• transmitting the sequential training data set and information on the first two-sided AI/ML model to a second training node,
• wherein the information on the first two-sided AI/ML model includes at least one of encoder model-related information or decoder model-related information.