Slice Sampling for Lattice Gaussian Distribution

Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain Monte Carlo (MCMC) is adopted to lattice Gaussian sampling. Firstly, the slice-based sampling algorithm is proposed to sample from lattice Gaus...

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Published in:Proceedings / IEEE International Symposium on Information Theory pp. 2589 - 2593
Main Authors: Wang, Zheng, Ling, Cong
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2019
Subjects:
Convergence
Encoding
Gaussian distribution
Gold
lattice coding and decoding
Lattice Gaussian sampling
Lattices
M-CMC methods
Markov processes
slice sampling
Zinc
ISSN:2157-8117
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Abstract Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain Monte Carlo (MCMC) is adopted to lattice Gaussian sampling. Firstly, the slice-based sampling algorithm is proposed to sample from lattice Gaussian distribution. Then, we demonstrate that the Markov chain arising from it is uniformly ergodic, namely, it converges exponentially fast to the stationary distribution. Moveover, the convergence rate of the underlying Markov chain is investigated, and we show the proposed slice sampling algorithm entails a better convergence performance than the independent Metropolis-Hastings-Klein (IMHK) sampling algorithm. Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement.
AbstractList Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain Monte Carlo (MCMC) is adopted to lattice Gaussian sampling. Firstly, the slice-based sampling algorithm is proposed to sample from lattice Gaussian distribution. Then, we demonstrate that the Markov chain arising from it is uniformly ergodic, namely, it converges exponentially fast to the stationary distribution. Moveover, the convergence rate of the underlying Markov chain is investigated, and we show the proposed slice sampling algorithm entails a better convergence performance than the independent Metropolis-Hastings-Klein (IMHK) sampling algorithm. Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement.
Author Ling, Cong
Wang, Zheng
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  givenname: Cong
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  fullname: Ling, Cong
  organization: Department of EEE, Imperial College London, London, SW7 2AZ, United Kingdom
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Snippet Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain...
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StartPage 2589
SubjectTerms Convergence
Encoding
Gaussian distribution
Gold
lattice coding and decoding
Lattice Gaussian sampling
Lattices
M-CMC methods
Markov processes
slice sampling
Zinc
Title Slice Sampling for Lattice Gaussian Distribution
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