Learnable Markov Chain Monte Carlo Sampling Methods for Lattice Gaussian Distribution

As a key ingredient of machine learning and artificial intelligence, the sampling algorithms with respect to lattice Gaussian distribution has emerged as an important problem in coding and decoding of wireless communications. In this paper, based on the conventional Gibbs sampling, the learnable del...

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Veröffentlicht in:	IEEE access Jg. 7; S. 87494 - 87503
Hauptverfasser:	Wang, Zheng, Lyu, Shanxiang, Liu, Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial intelligence Complexity Complexity theory Convergence Decoding Encoding Gaussian distribution Gibbs sampler decoding Lattice coding and decoding lattice Gaussian sampling Lattices Machine learning Markov analysis Markov chain Monte Carlo Markov chains Markov processes MIMO (control systems) Monte Carlo simulation Normal distribution Sampling methods Statistical analysis Wireless communications
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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