{"id":"https://openalex.org/W7127565329","doi":"https://doi.org/10.1109/ccnc65079.2026.11366348","title":"Spatio-Temporal SSB Prediction Using Deep Neural Network in 5G Advanced Networks","display_name":"Spatio-Temporal SSB Prediction Using Deep Neural Network in 5G Advanced Networks","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7127565329","doi":"https://doi.org/10.1109/ccnc65079.2026.11366348"},"language":null,"primary_location":{"id":"doi:10.1109/ccnc65079.2026.11366348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC)","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/A5023937234","display_name":"Digvijay Katyal","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Digvijay Katyal","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125062347","display_name":"Prince Arya","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Prince Arya","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022411228","display_name":"Kwangchul Kim","orcid":"https://orcid.org/0000-0002-6189-5237"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwangchul Kim","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121816591","display_name":"Nenavath Srinu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Nenavath Srinu","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089385513","display_name":"Sarvesha Anegundi Ganapathi","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sarvesha Anegundi Ganapathi","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124983688","display_name":"Hyunkuk Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunkuk Choi","raw_affiliation_strings":["Samsung Electronics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11302682,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.2727000117301941,"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"}},"topics":[{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.2727000117301941,"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"}},{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.18950000405311584,"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/T12079","display_name":"IoT Networks and Protocols","score":0.066600002348423,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5631999969482422},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5224000215530396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49729999899864197},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4860000014305115},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.48429998755455017},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4544999897480011},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.42800000309944153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225000262260437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5631999969482422},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5090000033378601},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49729999899864197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49380001425743103},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.4925000071525574},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.48429998755455017},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.39239999651908875},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C2989104859","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery capacity","level":4,"score":0.37599998712539673},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2799000144004822},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C2983254600","wikidata":"https://www.wikidata.org/wiki/Q1096907","display_name":"Power grid","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccnc65079.2026.11366348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8860477209091187,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2929287013","https://openalex.org/W3001957043","https://openalex.org/W4200087207","https://openalex.org/W4389161404","https://openalex.org/W4391342126","https://openalex.org/W4402834666","https://openalex.org/W4413909784"],"related_works":[],"abstract_inverted_index":{"Effective":[0],"Beam":[1],"Management":[2],"(BM)":[3],"in":[4,15,71,83,129],"5G":[5],"advanced":[6],"networks":[7],"is":[8],"essential":[9],"for":[10,124],"ensuring":[11],"reliable":[12],"connectivity,":[13],"especially":[14],"environments":[16],"characterized":[17],"by":[18],"high":[19],"mobility":[20],"and":[21,41,74,109,144,153],"varying":[22],"signal":[23],"conditions.":[24],"Traditional":[25],"methods":[26],"depend":[27],"on":[28],"frequent":[29],"measurements":[30],"of":[31,90],"Synchronization":[32],"Signal":[33,67],"Block":[34],"(SSB)":[35],"quality":[36],"which":[37,115],"increases":[38],"power":[39,98,154],"consumption":[40],"negatively":[42],"impact":[43],"battery":[44,103],"life.":[45,104],"In":[46],"this":[47],"paper,":[48],"we":[49],"propose":[50],"a":[51,130],"novel":[52],"spatiotemporal":[53],"prediction":[54,132],"model":[55,86,106],"that":[56],"leverages":[57],"Deep":[58],"Learning":[59],"(DL)":[60],"techniques":[61],"to":[62,139,147],"forecast":[63],"future":[64],"SSB":[65,81,92],"Received":[66,68],"Power":[69],"(RSRP)":[70],"both":[72],"space":[73],"time":[75],"domain.":[76],"By":[77],"predicting":[78],"the":[79,88,137,142],"best":[80],"index":[82],"advance,":[84],"our":[85],"eliminates":[87],"need":[89],"continuous":[91],"measurements.":[93],"This":[94],"approach":[95],"significantly":[96],"reduces":[97],"consumption,":[99],"thereby":[100],"enhancing":[101],"device":[102],"The":[105],"combines":[107],"Convolutional":[108],"Long":[110],"Short-Term":[111],"Memory":[112],"(LSTM)":[113],"networks,":[114],"capture":[116],"temporal":[117],"dependencies,":[118],"with":[119],"Fully":[120],"Connected":[121],"(FC)":[122],"network":[123],"spatial":[125],"feature":[126],"extraction,":[127],"resulting":[128],"robust":[131],"mechanism.":[133],"We":[134],"also":[135],"provided":[136],"mechanism":[138,146],"choose":[140],"between":[141,150],"DL":[143],"legacy":[145],"have":[148],"balance":[149],"global":[151],"performance":[152],"consumption.":[155]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-02-06T00:00:00"}