{"id":"https://openalex.org/W7036445486","doi":null,"title":"Channel prediction through machine learning techniques : A deep learning approach of forecasting channel state information over time","display_name":"Channel prediction through machine learning techniques : A deep learning approach of forecasting channel state information over time","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W7036445486"},"language":"en","primary_location":{"id":"pmh:oai:DiVA.org:uu-507527","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-507527","pdf_url":"https://uu.diva-portal.org/smash/get/diva2:1781558/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://uu.diva-portal.org/smash/get/diva2:1781558/FULLTEXT01","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kainulainen, Anton","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kainulainen, Anton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.5019999742507935,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.5019999742507935,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.14110000431537628,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10575","display_name":"Wireless Communication Networks Research","score":0.03420000150799751,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/channel-prediction","display_name":"channel prediction","score":0.9110000133514404},{"id":"https://openalex.org/keywords/rayleigh-fading","display_name":"Rayleigh fading","score":0.8460000157356262},{"id":"https://openalex.org/keywords/lstm-neural-network","display_name":"LSTM neural network","score":0.7580000162124634},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":1.0},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.6650000214576721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7063000202178955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7023000121116638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7005000114440918},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6244999766349792},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6152999997138977},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5641999840736389},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.5489000082015991},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5351999998092651},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.3977999985218048},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:DiVA.org:uu-507527","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-507527","pdf_url":"https://uu.diva-portal.org/smash/get/diva2:1781558/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"}],"best_oa_location":{"id":"pmh:oai:DiVA.org:uu-507527","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-507527","pdf_url":"https://uu.diva-portal.org/smash/get/diva2:1781558/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324119","display_name":"Uppsala Universitet","ror":"https://ror.org/048a87296"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7036445486.pdf","grobid_xml":"https://content.openalex.org/works/W7036445486.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2008179369","https://openalex.org/W2009597763","https://openalex.org/W2064675550","https://openalex.org/W2112796928","https://openalex.org/W2145856713","https://openalex.org/W2172206432","https://openalex.org/W2400238828","https://openalex.org/W2562947506","https://openalex.org/W2810482148","https://openalex.org/W2955170439","https://openalex.org/W2962883549","https://openalex.org/W2964121744","https://openalex.org/W2982127434","https://openalex.org/W3000769608"],"related_works":["https://openalex.org/W6959927595","https://openalex.org/W3013008552","https://openalex.org/W3007805730","https://openalex.org/W2780038432","https://openalex.org/W4391547833","https://openalex.org/W4415188572","https://openalex.org/W2953855414","https://openalex.org/W4396506160","https://openalex.org/W4411688091","https://openalex.org/W6986544496"],"abstract_inverted_index":{"Understanding":[0],"the":[1,18,41,48,70,83,92,95,116,136,159,166,182,206,219,225,228,249,261,282,288,301],"characteristics":[2],"of":[3,14,50,73,94,120,168,212,263,285,290,303],"a":[4,9,36,55,63,121,124,209,217,246],"wireless":[5,15,297],"radio":[6,191],"channel":[7,45,98,117,192],"is":[8,202,244],"fundamental":[10],"and":[11,62,107,151,242,270],"challenging":[12],"aspect":[13],"communication.":[16],"Through":[17],"years,":[19],"numerous":[20],"conventional":[21],"signal":[22],"processingtechniques":[23],"have":[24],"been":[25],"developed":[26],"to":[27,77,134,198,237,253,281,292],"predict":[28],"Channel":[29],"State":[30],"Information":[31],"(CSI).":[32],"This":[33],"thesis":[34],"introduces":[35],"new":[37],"approach":[38],"for":[39,272],"forecasting":[40,273],"dynamic":[42],"Rayleigh":[43,275],"fading":[44,276],"by":[46,175],"harnessing":[47],"power":[49],"deep":[51,265],"learning":[52,141,266],"techniques,":[53,267],"namely":[54],"Long":[56],"Short-Term":[57],"Memory":[58],"(LSTM)":[59],"neural":[60,74,96],"network":[61],"Convolutional":[64],"Neural":[65],"Network":[66],"(CNN).":[67],"By":[68],"leveraging":[69],"inherent":[71],"ability":[72],"networks":[75],"(NNs)":[76],"identify":[78,188],"non-linear":[79,147],"patterns":[80,148,189,295],"in":[81,104,144,149,190,296],"data,":[82,299],"proposed":[84],"method":[85],"offers":[86],"an":[87],"innovative":[88],"solution.":[89],"To":[90],"evaluate":[91],"performance":[93,233],"network-based":[97],"predictors,":[99],"computer":[100],"simulations":[101],"are":[102,113,227,251],"conducted":[103],"both":[105],"noise-free":[106],"noisy,":[108],"time-variant":[109,274],"scenarios.":[110],"The":[111,131],"results":[112],"compared":[114],"against":[115],"prediction":[118,210],"capabilities":[119],"linear":[122,156,195,305],"filter,":[123],"Normalized":[125],"Least":[126],"Mean":[127],"Squares":[128],"(NLMS)":[129],"filter.":[130],"goal":[132],"was,":[133],"assess":[135],"prevailing":[137],"assertion":[138],"that":[139,184,194,204,224,248],"machine":[140],"predictors":[142,196,250],"excel":[143],"discovering":[145],"complex":[146,294],"data":[150,193],"generally":[152],"outperform":[153],"traditional":[154,304],"model-based":[155],"solutions.":[157],"Remarkably,":[158],"NNs":[160,185,207,226,291],"yield":[161],"impressive":[162],"results,":[163],"even":[164],"surpassing":[165,300],"benchmark":[167],"-8":[169],"dB":[170,214],"at":[171,215],"half":[172,216],"awavelength":[173],"set":[174],"previous":[176],"research":[177,259],"papers.":[178],"These":[179,278],"finding":[180],"confirmed":[181],"hypothesis":[183],"can":[186],"effectively":[187],"struggle":[197],"detect.":[199],"However,":[200],"it":[201],"noted":[203],"while":[205],"achieved":[208],"accuracy":[211],"-10":[213],"wavelength,":[218],"author":[220],"cannot":[221],"conclusively":[222],"claim":[223],"superior":[229],"predictors.":[230,306],"Their":[231],"enhanced":[232],"may":[234],"be":[235],"attributed":[236],"their":[238],"higher":[239],"computational":[240],"complexity,":[241],"there":[243],"also":[245],"risk":[247],"subjected":[252],"overfitting.":[254],"In":[255],"conclusion,":[256],"this":[257],"theses":[258],"highlights":[260],"potential":[262],"employing":[264],"specifically":[268],"LSTM":[269],"CNN,":[271],"channels.":[277],"findings":[279],"contribute":[280],"growing":[283],"body":[284],"evidence":[286],"supporting":[287],"capability":[289],"uncover":[293],"communication":[298],"limitations":[302]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
