Methods and apparatuses for training and using multi-task machine learning models for communication of channel state information data

A method of training a second machine learning (ML) model associated with a first wireless device, the method comprising:

• encoding, using first parameters of the second ML model, a first channel state information (CSI), training data set (H1), and an identification of a first vendor to generate a first latent space representation;
• transmitting the first latent space representation to a first network node;
• classifying, using second parameters of the second ML model, the first CSI training data set and the identification of the first vendor to generate an estimated classification;
• determining a first loss based on the estimated classification and a true classification;
• and
• updating the first parameters and the second parameters based on the determined first loss.

A training apparatus for training a first machine learning (ML) model, the training apparatus comprising processing circuitry configured to cause the training apparatus to:

• receive a first latent space representation of a first channel state information (CSI), training data set (H1), from a first wireless device;
• decode, using first parameters of the first ML model, the first latent space representation to determine a first reconstructed CSI data set;
• classify, using second parameters of the first ML model, the first latent space representation to estimate an estimated classification;
• determine a first loss based on the estimated classification and a true classification;
• and update the first parameters and the second parameters based on the determined first loss.

A training apparatus for training a second machine learning (ML) model, the training apparatus comprising processing circuitry configured to cause the training apparatus to:

• encode using first parameters of the second ML model, a first channel state information (CSI), training data set (H1), and an identification of a first vendor to generate a first latent space representation;
• transmit the first latent space representation to a first network node;
• classify, using second parameters of the second ML model, the first CSI training data set and the identification of the first vendor to generate an estimated classification;
• determine a first loss based on the estimated classification and a true classification;
• and update the first parameters and the second parameters based on the determined first loss.