Imperial College London
Alternate

DrCongLing

Faculty of Engineering, Department of Electrical and Electronic Engineering

Reader in Coding and Information Theory
 
 
 
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Contact

 

+44 (0)20 7594 6214c.ling

 
 
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Location

 

815Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wang:2018:10.1109/TIT.2017.2742509,
author = {Wang, Z and Ling, C},
doi = {10.1109/TIT.2017.2742509},
journal = {IEEE Transactions on Information Theory},
pages = {738--751},
title = {On the geometric ergodicity of Metropolis-Hastings algorithms for lattice Gaussian sampling},
url = {http://dx.doi.org/10.1109/TIT.2017.2742509},
volume = {64},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding, and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain Monte Carlo methods is adopted for lattice Gaussian sampling. Two MH-based algorithms are proposed, which overcome the limitation of Klein's algorithm. The first one, referred to as the independent Metropolis-Hastings-Klein (MHK) algorithm, establishes a Markov chain via an independent proposal distribution. We show that the Markov chain arising from this independent MHK algorithm is uniformly ergodic, namely, it converges to the stationary distribution exponentially fast regardless of the initial state. Moreover, the rate of convergence is analyzed in terms of the theta series, leading to predictable mixing time. A symmetric Metropolis-Klein algorithm is also proposed, which is proven to be geometrically ergodic.
AU  - Wang,Z
AU  - Ling,C
DO  - 10.1109/TIT.2017.2742509
EP  - 751
PY  - 2018///
SN  - 0018-9448
SP  - 738
TI  - On the geometric ergodicity of Metropolis-Hastings algorithms for lattice Gaussian sampling
T2  - IEEE Transactions on Information Theory
UR  - http://dx.doi.org/10.1109/TIT.2017.2742509
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000422916500004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8013823
UR  - http://hdl.handle.net/10044/1/63026
VL  - 64
ER  -
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