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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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 87494 - 87503
Main Authors: Wang, Zheng, Lyu, Shanxiang, Liu, Ling
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 delayed metropolis-within-Gibbs (LDMWG) sampling algorithm is proposed to improve the convergence performance, which fully takes the advantages of the acceptance mechanism from the metropolis-hastings (MH) algorithm in the Markov chain Monte Carlo (MCMC) methods. The rejected candidate by the acceptance mechanism is utilized as a learnable experience for the generation of a new candidate at the same Markov move. In this way, the overall probability of remaining the same state at the Markov chain is greatly reduced, which leads to an improved convergence performance in the sense of Peskun ordering. Moreover, in order to reduce the complexity cost during the Markov mixing, a symmetric sampling structure which greatly simplified the sampling operation is further introduced and the symmetric learnable delayed metropolis-within-Gibbs (SLDMWG) sampling algorithm is given. Finally, the simulation results based on multi-input multi-output (MIMO) detections are presented to confirm the convergence gain and the complexity reduction brought by the proposed sampling schemes.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2925530