{"id":"https://openalex.org/W4415821243","doi":"https://doi.org/10.1109/lcomm.2025.3628069","title":"Dynamic Beam Prediction Optimization Using Transformer-Based Multimodal Perception","display_name":"Dynamic Beam Prediction Optimization Using Transformer-Based Multimodal Perception","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4415821243","doi":"https://doi.org/10.1109/lcomm.2025.3628069"},"language":null,"primary_location":{"id":"doi:10.1109/lcomm.2025.3628069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2025.3628069","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-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":null,"display_name":"Xingyi Zhang","orcid":"https://orcid.org/0009-0009-6644-2008"},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingyi Zhang","raw_affiliation_strings":["College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China"],"raw_orcid":"https://orcid.org/0009-0009-6644-2008","affiliations":[{"raw_affiliation_string":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China","institution_ids":["https://openalex.org/I135237710"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135237710"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1630549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.4133000075817108,"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"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.4133000075817108,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.15809999406337738,"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/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.11810000240802765,"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/codebook","display_name":"Codebook","score":0.5784000158309937},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5504000186920166},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.541100025177002},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5299000144004822},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.5160999894142151},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.4415000081062317},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.42570000886917114},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4016999900341034}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795000076293945},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.5784000158309937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.573199987411499},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.541100025177002},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.5160999894142151},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C184652730","wikidata":"https://www.wikidata.org/wiki/Q2357982","display_name":"Attenuation","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35910001397132874},{"id":"https://openalex.org/C168834538","wikidata":"https://www.wikidata.org/wiki/Q3705329","display_name":"Beam (structure)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2632000148296356},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lcomm.2025.3628069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2025.3628069","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2912129291","https://openalex.org/W2963408914","https://openalex.org/W2968810373","https://openalex.org/W3118230353","https://openalex.org/W3133743901","https://openalex.org/W3161745335","https://openalex.org/W3196023632","https://openalex.org/W3203970474","https://openalex.org/W3213965409","https://openalex.org/W4226381701","https://openalex.org/W4379382376","https://openalex.org/W4386453772","https://openalex.org/W4387870868","https://openalex.org/W4400111285","https://openalex.org/W4402205836","https://openalex.org/W4406680079","https://openalex.org/W4409761475","https://openalex.org/W4409889612"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,91,98],"growing":[2],"use":[3],"of":[4],"high-frequency":[5],"signals":[6,11],"in":[7,17],"6G":[8,93],"networks,":[9],"these":[10],"suffer":[12],"significant":[13],"interference":[14],"and":[15,42,50],"attenuation":[16],"dynamic":[18,30,59],"environments,":[19],"making":[20],"accurate":[21],"beam":[22,31,87],"prediction":[23,32],"challenging.":[24],"This":[25],"paper":[26],"presents":[27],"a":[28,43,58,107],"multimodal":[29,82],"framework":[33,55,99],"that":[34,63],"integrates":[35],"an":[36],"ST-Transformer":[37],"for":[38],"spatiotemporal":[39],"feature":[40,67],"extraction":[41],"CNN-Transformer":[44],"fusion":[45],"module":[46],"to":[47,71,84],"capture":[48],"local":[49],"global":[51],"contextual":[52],"dependencies.":[53],"The":[54,74],"further":[56],"includes":[57],"channel":[60],"pruning":[61],"mechanism":[62],"adaptively":[64],"removes":[65],"redundant":[66],"channels":[68],"during":[69],"inference":[70,110],"optimize":[72],"computation.":[73],"Intelligent":[75],"Beam":[76],"Codebook":[77],"Dynamic":[78],"Optimizer":[79],"(IBCDO)":[80],"fuses":[81],"inputs":[83],"dynamically":[85],"update":[86],"weights.":[88],"Experiments":[89],"on":[90],"DeepSense":[92],"Scenario":[94],"9":[95],"dataset":[96],"show":[97],"achieves":[100],"82.6%":[101],"accuracy":[102],"under":[103],"complex":[104],"conditions":[105],"with":[106],"210":[108],"ms":[109],"delay.":[111]},"counts_by_year":[],"updated_date":"2025-12-19T00:32:22.182498","created_date":"2025-11-03T00:00:00"}