Publications in Scientific Journals:

S. Schwarz, T. Tsiftsis:
"Codebook Training for Trellis-Based Hierarchical Grassmannian Classification";
IEEE Wireless Communications Letters, 11 (2022), 3; 636 - 640.

English abstract:
We consider classification of points on a complex-valued Grassmann manifold of m-dimensional subspaces within the n-dimensional complex Euclidean space. We introduce a trellis-based hierarchical classification network, which is based on an orthogonal product decomposition of the orthogonal basis representing the m-dimensional subspace. Exploiting the similarity of the proposed trellis classifier with a neural network, we propose stochastic gradient-based training techniques. We apply the proposed methods to two important applications in wireless communication, namely Grassmannian channel state information quantization in multiple-input multiple-output communications and non-coherent Grassmannian multi-resolution transmission.

Grassmannian classification, CSI quantization, non-coherent transmission, trellis network training

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.