Feature vector compression for two-sided channel state information feedback models in wireless networks

An apparatus for wireless communication, comprising:

• at least one memory;
• and
• at least one processor coupled with the at least one memory and configured to cause the apparatus to:
• receive, from a first device, a first signaling indicating a first set of information;
• input, to a decoder neural network model, input data based on at least one of the first set of information and a first set of parameters;
• output, by the decoder neural network model, output data generated using the input data and a second set of information used to determine the decoder neural network model for decoding the input data.

A method, comprising:

• determining at least one feature vector of input data;
• factorizing, based on Khatri-Rao factorization, the at least one feature vector into one or more components wherein each component comprises a first factor vector and a second factor vector, and each of the first factor vector and the second factor vector has fewer elements than the feature vector;
• generating encoded information by encoding at least the first factor vector or the second factor vector in at least one of the components;
• and
• transmitting, to a first device, a first signaling indicating the encoded information.