Machine learning for csi feedback considering polarizations

Abstract

The present disclosure relates to machine learning for CSI feedback considering polarizations. In an aspect, a wireless device, comprising: at least one antenna; at least one radio coupled to the at least one antenna; and a processor coupled to the at least one radio; wherein the processor is configured to generate at least one matrix related to CSI feedback, wherein each matrix of the at least one matrix corresponds to one spatial layer of at least one spatial layer at the wireless device, and for each of the at least one matrix, the matrix comprises a plurality of vectors in a first dimension, a number of said plurality of vectors is the same as a size of a second dimension of the matrix, a number of elements in each of said plurality of vectors is the same as a size of the first dimension of the matrix, and said plurality of vectors belong to at least two groups, each group of vectors are associated with a same polarization of at least one antenna of a cellular base station, and said plurality of vectors are associated with two different polarizations of the at least one antenna of the cellular base station, wherein each of the at least one matrix is arranged by arranging said plurality of vectors in the first dimension according to the polarizations such that vectors associated with a same polarization are arranged next to each other; obtain an output from processing the arranged at least one matrix using a neural network (NN); and transmit, via the at least one radio, information indicating the output to the cellular base station.