{"id":"https://openalex.org/W4417129366","doi":"https://doi.org/10.48550/arxiv.2510.09314","title":"RadioFlow: Efficient Radio Map Construction Framework with Flow Matching","display_name":"RadioFlow: Efficient Radio Map Construction Framework with Flow Matching","publication_year":2025,"publication_date":"2025-10-10","ids":{"openalex":"https://openalex.org/W4417129366","doi":"https://doi.org/10.48550/arxiv.2510.09314"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.09314","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.09314","pdf_url":"https://arxiv.org/pdf/2510.09314","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.09314","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jia, Haozhe","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Haozhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013569204","display_name":"Wenshuo Chen","orcid":"https://orcid.org/0009-0002-1966-6059","observed_orcids":["https://orcid.org/0009-0002-1966-6059"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wenshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036982027","display_name":"Xiucheng Wang","orcid":"https://orcid.org/0000-0003-1439-4875","observed_orcids":["https://orcid.org/0000-0003-1439-4875"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiucheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050651525","display_name":"Nan Cheng","orcid":"https://orcid.org/0000-0001-7907-2071","observed_orcids":["https://orcid.org/0000-0001-7907-2071","https://orcid.org/0000-0002-2007-8468"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461085","display_name":"Hongbo Zhang","orcid":"https://orcid.org/0000-0001-7259-5419","observed_orcids":["https://orcid.org/0000-0001-7259-5419"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Hongbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yu, Kuimou","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Kuimou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086484127","display_name":"Songning Lai","orcid":"https://orcid.org/0009-0007-3132-9414","observed_orcids":["https://orcid.org/0009-0007-3132-9414"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lai, Songning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jia, Nanjian","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Nanjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tian, Bowen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Bowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao, Hongru","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Hongru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052861384","display_name":"Yutao Yue","orcid":"https://orcid.org/0000-0003-4532-0924","observed_orcids":["https://orcid.org/0000-0003-4532-0924"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Yutao","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":false,"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":false,"primary_topic":{"id":"https://openalex.org/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.7592999935150146,"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/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.7592999935150146,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.03519999980926514,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.029200000688433647,"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/flow-matching","display_name":"flow matching","score":0.9819999933242798},{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.8679999709129333},{"id":"https://openalex.org/keywords/6g-networks","display_name":"6G","score":0.9150000214576721},{"id":"https://openalex.org/keywords/inference-efficiency","display_name":"inference efficiency","score":0.5529999732971191},{"id":"https://openalex.org/keywords/parameter-efficiency","display_name":"parameter efficiency","score":0.5329999923706055},{"id":"https://openalex.org/keywords/wireless-channel-modeling","display_name":"wireless channel modeling","score":0.36000001430511475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6905999779701233},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6757000088691711},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5335000157356262},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.48590001463890076},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41659998893737793},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3569999933242798},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3424000144004822},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.3068000078201294},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29319998621940613},{"id":"https://openalex.org/C101541887","wikidata":"https://www.wikidata.org/wiki/Q1555675","display_name":"Remote radio head","level":4,"score":0.2912999987602234},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.2513999938964844},{"id":"https://openalex.org/C2986056383","wikidata":"https://www.wikidata.org/wiki/Q556030","display_name":"Power flow","level":4,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.09314","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.09314","pdf_url":"https://arxiv.org/pdf/2510.09314","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.09314","doi":"https://doi.org/10.48550/arxiv.2510.09314","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.09314","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.09314","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.09314","pdf_url":"https://arxiv.org/pdf/2510.09314","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6773999929428101}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,25,65,70,94,115],"real-time":[2,116],"radio":[3],"map":[4],"(RM)":[5],"generation":[6,49],"is":[7],"crucial":[8],"for":[9,120],"next-generation":[10],"wireless":[11],"systems,":[12],"yet":[13],"diffusion-based":[14,103],"approaches":[15],"often":[16],"suffer":[17],"from":[18],"large":[19],"model":[20],"sizes,":[21],"slow":[22],"iterative":[23],"denoising,":[24],"high":[26],"inference":[27,71],"latency,":[28],"which":[29],"hinder":[30],"practical":[31],"deployment.":[32],"To":[33],"overcome":[34],"these":[35],"limitations,":[36],"we":[37],"propose":[38],"\\textbf{RadioFlow},":[39],"a":[40,109],"novel":[41],"flow-matching-based":[42],"generative":[43],"framework":[44],"that":[45,83],"achieves":[46,85],"high-fidelity":[47],"RM":[48],"through":[50],"single-step":[51],"efficient":[52],"sampling.":[53],"Unlike":[54],"conventional":[55],"diffusion":[56],"models,":[57],"RadioFlow":[58,84],"learns":[59],"continuous":[60],"transport":[61],"trajectories":[62],"between":[63],"noise":[64],"data,":[66],"enabling":[67],"both":[68],"training":[69],"to":[72,90,100],"be":[73],"significantly":[74],"accelerated":[75],"while":[76],"preserving":[77],"reconstruction":[78],"accuracy.":[79],"Comprehensive":[80],"experiments":[81],"demonstrate":[82],"state-of-the-art":[86],"performance":[87],"with":[88],"\\textbf{up":[89],"8$\\times$":[91],"fewer":[92],"parameters}":[93],"\\textbf{over":[95],"4$\\times$":[96],"faster":[97],"inference}":[98],"compared":[99],"the":[101,126],"leading":[102],"baseline":[104],"(RadioDiff).":[105],"This":[106],"advancement":[107],"provides":[108],"promising":[110],"pathway":[111],"toward":[112],"scalable,":[113],"energy-efficient,":[114],"electromagnetic":[117],"digital":[118],"twins":[119],"future":[121],"6G":[122],"networks.":[123],"We":[124],"release":[125],"code":[127],"at":[128],"\\href{https://github.com/Hxxxz0/RadioFlow}{GitHub}.":[129]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-14T00:00:00"}