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

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

http://dx.doi.org/10.1109/LWC.2021.3139166

https://publik.tuwien.ac.at/files/publik_301185.pdf