Linear MIMO Precoders With Finite Alphabet Inputs via Stochastic Optimization and Deep Neural Networks (DNNs)

In this paper, we investigate designs of linear precoders for vector Gaussian channels via stochastic optimizations and deep neural networks (DNNs). We assume that the channel inputs are drawn from practical finite alphabets, and we search for precoders maximizing the mutual information between chan...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 69; pp. 4269 - 4281
Main Authors:	Jing, Shusen, Xiao, Chengshan
Format:	Journal Article
Language:	English
Published:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Alphabets Artificial neural networks autoencoders Bit error rate Codes deep neural networks Entropy (Information theory) finite alphabet High speed rail linear precoders Lower bounds Mathematical analysis Maximization MIMO MIMO communication Mutual information Neural networks Optimization Precoding Signal processing algorithms Task analysis Training
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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