{"id":"https://openalex.org/W7147398011","doi":"https://doi.org/10.1109/cnml68938.2026.11452366","title":"Performance Analysis of Diffusion LMS for Chirp Cyclostationary White Gaussian and Non-Gaussian Inputs","display_name":"Performance Analysis of Diffusion LMS for Chirp Cyclostationary White Gaussian and Non-Gaussian Inputs","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147398011","doi":"https://doi.org/10.1109/cnml68938.2026.11452366"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132577671","display_name":"Peng Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Cai","raw_affiliation_strings":["Southwest University,School of Electronic and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Electronic and Information Engineering,China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132554767","display_name":"Yunfei Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Zheng","raw_affiliation_strings":["Southwest University,School of Electronic and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Electronic and Information Engineering,China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086865721","display_name":"Xiong Liu","orcid":"https://orcid.org/0000-0003-2939-574X"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["Southwest University,School of Electronic and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Electronic and Information Engineering,China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132712894","display_name":"Qiangqiang Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I50180762","display_name":"Chongqing Three Gorges University","ror":"https://ror.org/05rs3pv16","country_code":"CN","type":"education","lineage":["https://openalex.org/I50180762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangqiang Zhang","raw_affiliation_strings":["Chongqing Three Gorges University,School of Computer Science and Engineering,China"],"affiliations":[{"raw_affiliation_string":"Chongqing Three Gorges University,School of Computer Science and Engineering,China","institution_ids":["https://openalex.org/I50180762"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5132577671"],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.89768251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"184","last_page":"189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.3698999881744385,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.3698999881744385,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.05480000004172325,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.043699998408555984,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/cyclostationary-process","display_name":"Cyclostationary process","score":0.9657999873161316},{"id":"https://openalex.org/keywords/kurtosis","display_name":"Kurtosis","score":0.7684999704360962},{"id":"https://openalex.org/keywords/chirp","display_name":"Chirp","score":0.695900022983551},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6011999845504761},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5853000283241272},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.47920000553131104},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.4659000039100647},{"id":"https://openalex.org/keywords/stationary-process","display_name":"Stationary process","score":0.41850000619888306},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.41040000319480896}],"concepts":[{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.9657999873161316},{"id":"https://openalex.org/C166963901","wikidata":"https://www.wikidata.org/wiki/Q287251","display_name":"Kurtosis","level":2,"score":0.7684999704360962},{"id":"https://openalex.org/C132794960","wikidata":"https://www.wikidata.org/wiki/Q27304","display_name":"Chirp","level":3,"score":0.695900022983551},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.657800018787384},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6011999845504761},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5853000283241272},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5300999879837036},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C110405555","wikidata":"https://www.wikidata.org/wiki/Q1192209","display_name":"Stationary process","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3384999930858612},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.33640000224113464},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.32919999957084656},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3192000091075897},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3179999887943268},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C123919525","wikidata":"https://www.wikidata.org/wiki/Q761942","display_name":"Frequency deviation","level":3,"score":0.31189998984336853},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2988709989","wikidata":"https://www.wikidata.org/wiki/Q85784623","display_name":"Mean square","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C135692309","wikidata":"https://www.wikidata.org/wiki/Q111124","display_name":"Square (algebra)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C105152959","wikidata":"https://www.wikidata.org/wiki/Q1074780","display_name":"Chirp spread spectrum","level":5,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/F4320324805","display_name":"Chongqing Municipal Education Commission","ror":"https://ror.org/031nm5713"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W338925043","https://openalex.org/W2093795816","https://openalex.org/W2146170879","https://openalex.org/W2395429589","https://openalex.org/W2436839239","https://openalex.org/W2530728613","https://openalex.org/W2963157432","https://openalex.org/W2968734241","https://openalex.org/W2972674561","https://openalex.org/W3021272668","https://openalex.org/W3022417564","https://openalex.org/W3118340616","https://openalex.org/W3134574653","https://openalex.org/W3138514088","https://openalex.org/W3200280542","https://openalex.org/W4244753254","https://openalex.org/W4285248276","https://openalex.org/W4312843998","https://openalex.org/W4381785350","https://openalex.org/W4389665404","https://openalex.org/W4391733760","https://openalex.org/W4400525237","https://openalex.org/W4404317077","https://openalex.org/W4408017192","https://openalex.org/W4408281791","https://openalex.org/W4408352661","https://openalex.org/W7081977729"],"related_works":[],"abstract_inverted_index":{"The":[0,24,103],"diffusion":[1],"least":[2],"mean":[3,74],"square":[4],"algorithm":[5],"(DLMS)":[6],"has":[7],"been":[8],"studied":[9],"with":[10,36,78,93,129],"the":[11,67,73,79,84,109,114,124,130],"chirp":[12,25],"cyclostationary":[13,26,34],"white":[14],"Gaussian":[15],"and":[16,21,40,61,75,135],"non-Gaussian":[17],"inputs":[18,132],"in":[19,59,72,133],"stationary":[20,60,134],"non-stationary":[22,62,136],"environments.":[23,63],"signal":[27],"(CCS)":[28],"is":[29],"a":[30,56],"generalized":[31],"form":[32],"of":[33,70,100,111,127],"signals":[35],"periodically":[37],"time-varying":[38],"power":[39],"its":[41],"frequency":[42],"changes":[43],"over":[44,138],"time.":[45],"For":[46],"performance":[47],"analysis,":[48],"random":[49],"walk":[50],"variable":[51],"parameters":[52],"are":[53,90],"used":[54],"as":[55],"general":[57,105],"case":[58],"This":[64],"paper":[65],"presents":[66],"theoretical":[68,125],"analysis":[69,106,126],"DLMS":[71,112,128],"meansquare":[76],"sense":[77],"CCS":[80,131],"input.":[81],"In":[82],"addition,":[83],"approximated":[85],"steady-state":[86],"mean-square":[87],"deviation":[88],"expressions":[89],"also":[91],"given":[92],"slow":[94],"input":[95,101,115],"variation":[96],"for":[97,123],"all":[98],"types":[99],"distributions.":[102],"derived":[104],"model":[107],"shows":[108],"dependence":[110],"on":[113],"kurtosis":[116],"coefficient.":[117],"Monte":[118],"Carlo":[119],"simulations":[120],"provide":[121],"supports":[122],"environments":[137],"multi-tasking":[139],"networks.":[140]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}
