Reinforcement Learning-Aided Markov Chain Monte Carlo For Lattice Gaussian Sampling

Sampling from the lattice Gaussian distribution has emerged as a key problem in coding, decoding and cryptography. In this paper, the Gibbs sampling from Markov chain Monte Carlo (MCMC) methods is investigated for lattice Gaussian sampling. Firstly, the error function of random scan Gibbs sampling i...

Full description

Saved in:
Bibliographic Details
Published in:2021 IEEE Information Theory Workshop (ITW) pp. 1 - 5
Main Authors: Wang, Zheng, Xia, Yili, Lyu, Shanxiang, Ling, Cong
Format: Conference Proceeding
Language:English
Published: IEEE 17.10.2021
Subjects:
Gaussian distribution
lattice coding and decoding
Lattice Gaussian sampling
Lattices
Markov chain Monte Carlo
Markov processes
MIMO detection
Monte Carlo methods
Performance gain
Reinforcement learning
Simulation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Sampling from the lattice Gaussian distribution has emerged as a key problem in coding, decoding and cryptography. In this paper, the Gibbs sampling from Markov chain Monte Carlo (MCMC) methods is investigated for lattice Gaussian sampling. Firstly, the error function of random scan Gibbs sampling is derived, and we show that it is partially determined by the selection probabilities over the sampling components. Then, in order to minimize the error function for a better sampling performance, a reinforcement learning mechanism is proposed for random scan Gibbs sampling to adaptively update the selection probabilities by learning from the random samples generated along with the chain. Finally, simulation results based on MIMO detection are presented to confirm the performance gain at the expense of limited complexity cost.
AbstractList Sampling from the lattice Gaussian distribution has emerged as a key problem in coding, decoding and cryptography. In this paper, the Gibbs sampling from Markov chain Monte Carlo (MCMC) methods is investigated for lattice Gaussian sampling. Firstly, the error function of random scan Gibbs sampling is derived, and we show that it is partially determined by the selection probabilities over the sampling components. Then, in order to minimize the error function for a better sampling performance, a reinforcement learning mechanism is proposed for random scan Gibbs sampling to adaptively update the selection probabilities by learning from the random samples generated along with the chain. Finally, simulation results based on MIMO detection are presented to confirm the performance gain at the expense of limited complexity cost.
Author Lyu, Shanxiang
Xia, Yili
Ling, Cong
Wang, Zheng
Author_xml – sequence: 1
  givenname: Zheng
  surname: Wang
  fullname: Wang, Zheng
  email: z.wang@ieee.org
  organization: Southeast University,School of Information Science and Engineering,Nanjing,China,210096
– sequence: 2
  givenname: Yili
  surname: Xia
  fullname: Xia, Yili
  email: yili_xia@seu.edu.cn
  organization: Southeast University,School of Information Science and Engineering,Nanjing,China,210096
– sequence: 3
  givenname: Shanxiang
  surname: Lyu
  fullname: Lyu, Shanxiang
  email: shanxianglyu@gmail.com
  organization: Jinan University,College of Cyber Security,Guangzhou,China,510632
– sequence: 4
  givenname: Cong
  surname: Ling
  fullname: Ling, Cong
  email: cling@ieee.org
  organization: Imperial College London,Department of EEE,London,United Kingdom,SW7 2AZ
BookMark eNotj81KAzEYACPowVafQIS8wK75azY5lsXWwhbBVjyWr8kXDe4mJbsKvr2CPc1pBmZGLlNOSMg9ZzXnzD5s9m_KWKlrwQSvreZccXFBZlzrhWKSC3NNdi8YU8jF4YBpoh1CSTG9V8vo0dMtlM_8TdsPiIluc5qQtlD6TFe50A6mKTqka_gaxwiJ7mA49X_yDbkK0I94e-acvK4e9-1T1T2vN-2yq6JgcqoWYJX1TCrQ0npAp6QRCNa5Bq06BgB3RCMAdRMCotbSyeAVA8WNbzzKObn770ZEPJxKHKD8HM6f8hd76U6Y
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ITW48936.2021.9611412
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE/IET Electronic Library
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL) - NZ
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 1665403128
9781665403122
EndPage 5
ExternalDocumentID 9611412
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  funderid: 10.13039/501100001809
– fundername: Natural Science Foundation of Jiangsu Province
  funderid: 10.13039/501100004608
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i203t-5a949d034a639daec4382ea9cc7e94bfaacbe82ae67ffee663c3fd40a418d7de3
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000794133300035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Thu Jun 29 18:37:52 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-5a949d034a639daec4382ea9cc7e94bfaacbe82ae67ffee663c3fd40a418d7de3
PageCount 5
ParticipantIDs ieee_primary_9611412
PublicationCentury 2000
PublicationDate 2021-Oct.-17
PublicationDateYYYYMMDD 2021-10-17
PublicationDate_xml – month: 10
  year: 2021
  text: 2021-Oct.-17
  day: 17
PublicationDecade 2020
PublicationTitle 2021 IEEE Information Theory Workshop (ITW)
PublicationTitleAbbrev ITW
PublicationYear 2021
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7875327
Snippet Sampling from the lattice Gaussian distribution has emerged as a key problem in coding, decoding and cryptography. In this paper, the Gibbs sampling from...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Gaussian distribution
lattice coding and decoding
Lattice Gaussian sampling
Lattices
Markov chain Monte Carlo
Markov processes
MIMO detection
Monte Carlo methods
Performance gain
Reinforcement learning
Simulation
Title Reinforcement Learning-Aided Markov Chain Monte Carlo For Lattice Gaussian Sampling
URI https://ieeexplore.ieee.org/document/9611412
WOSCitedRecordID wos000794133300035&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA21ePCk0orf5ODRtNnd7GZzlGJV0FJsxd7KJJnUguzKdtvf72a7VAQv3kIIBCZhZjJ57w0hN5WLS0NlgVlrYib8NQYRI0uUA625RAx13WxCjkbpbKbGLXK748IgYg0-w54f1n_5NjdrXyrrq6TK3n1L4T0pky1XqyHlBFz1n6bvXknF4w7CoNes_dU0pY4Zw8P_7XZEuj_kOzrehZVj0sKsQyavWEucmrqaRxtV1AW7W1q01BNu8g0dfFTPfPri9aboAIrPnA7zgj5D6QFu9AHWK8-YpBPwKPJs0SVvw_vp4JE1_RDYMuRRyWJQQlkeCajSCgto_CcegjJGohLaARiNaQiYSOcQq1zCRM4KDiJIrbQYnZB2lmd4SmjiLA9Sgy4WIFBprSPO0QAPFQonzRnpeIPMv7aSF_PGFud_T1-QA29z79IDeUnaZbHGK7JvNuVyVVzX5_QNTs-YUg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA2lCnpSacVvc_Dottnd7EeOUqwttqXYir2VSTJpC7Ir221_v5vtUhG8eAshEJiEmcnkvTeEPBQuLvaEBkdrFTjcXmPgATqhMCAlixA9WTabiEajeDYT4xp53HNhELEEn2HLDsu_fJ2qjS2VtUVYZO-2pfBBwLnHdmytipbjMtHuTz-slopFHnhuq1r9q21KGTW6J__b75Q0f-h3dLwPLGekhkmDTN6wFDlVZT2PVrqoC-dppVFTS7lJt7SzLB76dGgVp2gHss-UdtOMDiC3EDf6Apu15UzSCVgcebJokvfu87TTc6qOCM7KY37uBCC40MznUCQWGlDZbzwEoVSEgksDoCTGHmAYGYNYZBPKN5oz4G6sI43-OaknaYIXhIZGMzdWaAIOHIWU0mcMFTBPIDeRuiQNa5D51070Yl7Z4urv6Xty1JsOB_NBf_R6TY6t_a2Dd6MbUs-zDd6SQ7XNV-vsrjyzb2gpm5k
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2021+IEEE+Information+Theory+Workshop+%28ITW%29&rft.atitle=Reinforcement+Learning-Aided+Markov+Chain+Monte+Carlo+For+Lattice+Gaussian+Sampling&rft.au=Wang%2C+Zheng&rft.au=Xia%2C+Yili&rft.au=Lyu%2C+Shanxiang&rft.au=Ling%2C+Cong&rft.date=2021-10-17&rft.pub=IEEE&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FITW48936.2021.9611412&rft.externalDocID=9611412