期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 11, 期 3, 页码 636-640出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3139166
关键词
Grassmannian classification; CSI quantization; non-coherent transmission; trellis network training
This paper discusses the classification of points on a complex-valued Grassmann manifold in an n-dimensional complex Euclidean space. A trellis-based hierarchical classification network is introduced, which is based on the orthogonal product decomposition of the orthogonal basis representing the m-dimensional subspace. Stochastic gradient-based training techniques are proposed by exploiting the similarity between the proposed trellis classifier and a neural network. The proposed methods are applied to important applications in wireless communication, including Grassmannian channel state information quantization in multiple-input multiple-output communications and non-coherent Grassmannian multi-resolution transmission.
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.
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