{"id":"https://openalex.org/W3216246839","doi":"https://doi.org/10.1109/itw48936.2021.9611412","title":"Reinforcement Learning-Aided Markov Chain Monte Carlo For Lattice Gaussian Sampling","display_name":"Reinforcement Learning-Aided Markov Chain Monte Carlo For Lattice Gaussian Sampling","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3216246839","doi":"https://doi.org/10.1109/itw48936.2021.9611412","mag":"3216246839"},"language":"en","primary_location":{"id":"doi:10.1109/itw48936.2021.9611412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itw48936.2021.9611412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Information Theory Workshop (ITW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028761238","display_name":"Zheng Wang","orcid":"https://orcid.org/0009-0008-0462-9291"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005242197","display_name":"Yili Xia","orcid":"https://orcid.org/0000-0002-4402-8131"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yili Xia","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021761593","display_name":"Shanxiang Lyu","orcid":"https://orcid.org/0000-0002-5005-5056"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanxiang Lyu","raw_affiliation_strings":["College of Cyber Security, Jinan University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Cyber Security, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065795716","display_name":"Cong Ling","orcid":"https://orcid.org/0000-0001-7873-4862"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Cong Ling","raw_affiliation_strings":["Department of EEE, Imperial College London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of EEE, Imperial College London, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028761238"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16908261,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7987979650497437},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6863216161727905},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.5647943615913391},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5521001815795898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5506187081336975},{"id":"https://openalex.org/keywords/rejection-sampling","display_name":"Rejection sampling","score":0.5323902368545532},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4924679696559906},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4714815616607666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4708862900733948},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4548746347427368},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4198760688304901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.414958119392395},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.3953331708908081},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3312534689903259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27703553438186646},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27145668864250183},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06762883067131042}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7987979650497437},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6863216161727905},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.5647943615913391},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5521001815795898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5506187081336975},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.5323902368545532},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4924679696559906},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4714815616607666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4708862900733948},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4548746347427368},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4198760688304901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.414958119392395},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3953331708908081},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3312534689903259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27703553438186646},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27145668864250183},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06762883067131042},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itw48936.2021.9611412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itw48936.2021.9611412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Information Theory Workshop (ITW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1515461433","https://openalex.org/W1994726788","https://openalex.org/W2001093624","https://openalex.org/W2031533839","https://openalex.org/W2047040233","https://openalex.org/W2064158368","https://openalex.org/W2078014848","https://openalex.org/W2090304286","https://openalex.org/W2114664664","https://openalex.org/W2164654926","https://openalex.org/W2883749581","https://openalex.org/W2953511316","https://openalex.org/W2962810704","https://openalex.org/W2962985151","https://openalex.org/W2963558938","https://openalex.org/W3097571055","https://openalex.org/W3101377043","https://openalex.org/W3104675979","https://openalex.org/W4214717370","https://openalex.org/W6715131761","https://openalex.org/W6784893827"],"related_works":["https://openalex.org/W2997752974","https://openalex.org/W1546022168","https://openalex.org/W4226314133","https://openalex.org/W2592308920","https://openalex.org/W1539868720","https://openalex.org/W2135966539","https://openalex.org/W2914481316","https://openalex.org/W1526222228","https://openalex.org/W4300877486","https://openalex.org/W4385281907"],"abstract_inverted_index":{"Sampling":[0],"from":[1,23,95],"the":[2,20,37,56,60,68,90,96,102,115,119],"lattice":[3,33],"Gaussian":[4,34],"distribution":[5],"has":[6],"emerged":[7],"as":[8],"a":[9,72,76],"key":[10],"problem":[11],"in":[12,64],"coding,":[13],"decoding":[14],"and":[15,47],"cryptography.":[16],"In":[17],"this":[18],"paper,":[19],"Gibbs":[21,43,85],"sampling":[22,44,61,74,86],"Markov":[24],"chain":[25],"Monte":[26],"Carlo":[27],"(MCMC)":[28],"methods":[29],"is":[30,45,52,80],"investigated":[31],"for":[32,71,82],"sampling.":[35],"Firstly,":[36],"error":[38,69],"function":[39,70],"of":[40,121],"random":[41,83,97],"scan":[42,84],"derived,":[46],"we":[48],"show":[49],"that":[50],"it":[51],"partially":[53],"determined":[54],"by":[55,93],"selection":[57,91],"probabilities":[58,92],"over":[59],"components.":[62],"Then,":[63],"order":[65],"to":[66,87,113],"minimize":[67],"better":[73],"performance,":[75],"reinforcement":[77],"learning":[78,94],"mechanism":[79],"proposed":[81],"adaptively":[88],"update":[89],"samples":[98],"generated":[99],"along":[100],"with":[101],"chain.":[103],"Finally,":[104],"simulation":[105],"results":[106],"based":[107],"on":[108],"MIMO":[109],"detection":[110],"are":[111],"presented":[112],"confirm":[114],"performance":[116],"gain":[117],"at":[118],"expense":[120],"limited":[122],"complexity":[123],"cost.":[124]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}