{"id":"https://openalex.org/W7148465757","doi":"https://doi.org/10.48550/arxiv.2604.00676","title":"DF-3DRME: A Data-Friendly Learning Framework for 3D Radio Map Estimation based on Super-Resolution Technique","display_name":"DF-3DRME: A Data-Friendly Learning Framework for 3D Radio Map Estimation based on Super-Resolution Technique","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148465757","doi":"https://doi.org/10.48550/arxiv.2604.00676"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00676","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00676","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,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00676","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132816395","display_name":"Lin Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132806378","display_name":"Weifeng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Weifeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132807286","display_name":"Shuowen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132807463","display_name":"Giuseppe Caire","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caire, Giuseppe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132788753","display_name":"Liang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Liang","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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.33180001378059387,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.33180001378059387,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.1251000016927719,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.08910000324249268,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5877000093460083},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5507000088691711},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5375000238418579},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5166000127792358},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4648999869823456},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.436599999666214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122000217437744},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5877000093460083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4551999866962433},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.436599999666214},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3327000141143799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3028999865055084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C2989466925","wikidata":"https://www.wikidata.org/wiki/Q1061197","display_name":"Radio networks","level":4,"score":0.2775999903678894}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00676","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00676","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,"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":"doi:10.48550/arxiv.2604.00676","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00676","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,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5313099026679993,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-Resolution":[0],"three-dimensional":[1],"(3D)":[2],"radio":[3,11,164],"maps":[4,92],"(RMs)":[5],"provide":[6],"rich":[7],"information":[8],"about":[9],"the":[10,23,48,51,83,101,127,132,144,148,153,162,167,177,181,191,193,201,209,216,227,230],"landscape":[12],"that":[13,215],"is":[14,55,66],"essential":[15],"to":[16,67,100,138,179,186],"a":[17,69,79,87,113,140,158],"myriad":[18],"of":[19,53,90,95,103,106,161,226],"wireless":[20,25],"applications":[21],"in":[22,36,47,59,126,208,229],"future":[24],"networks.":[26],"Although":[27],"deep":[28],"learning":[29],"(DL)":[30],"methods":[31],"have":[32],"shown":[33],"their":[34],"effectiveness":[35],"RM":[37,45,73,136,151,185,205,235],"construction,":[38],"existing":[39],"approaches":[40],"require":[41],"massive":[42],"high-resolution":[43,71,188,203,233],"3D":[44,72,115,135,150,184,204,234],"samples":[46,137,206],"training":[49,77],"dataset,":[50,81],"acquisition":[52,241],"which":[54,120,156,237],"labor-intensive":[56],"and":[57,243],"time-consuming":[58],"practice.":[60],"In":[61,166],"this":[62,109],"paper,":[63],"our":[64],"goal":[65],"devise":[68],"data-friendly":[70],"construction":[74],"solution":[75],"via":[76],"over":[78],"hybrid":[80,210],"wherein":[82],"RMs":[84],"associated":[85],"with":[86,199,223],"small":[88],"fraction":[89],"environment":[91],"(EMs)":[93],"are":[94,105],"high-resolution,":[96],"while":[97],"those":[98],"corresponding":[99],"majority":[102],"EMs":[104,228],"low-resolution.":[107],"To":[108],"end,":[110],"we":[111,130,170],"propose":[112],"Data-Friendly":[114],"Radio":[116],"Map":[117],"Estimator":[118],"(DF-3DRME),":[119],"comprises":[121],"two":[122],"processing":[123],"stages.":[124],"Specifically,":[125],"first":[128],"stage,":[129,169],"leverage":[131],"abundant":[133],"low-resolution":[134,149,183],"train":[139],"neural":[141],"network,":[142,175],"termed":[143,176],"LR-Net,":[145,192],"for":[146],"predicting":[147],"from":[152],"input":[154],"EM,":[155],"provides":[157],"coarse":[159],"characterization":[160],"spatial":[163],"propagation.":[165],"second":[168],"employ":[171],"an":[172],"advanced":[173],"super-resolution":[174],"SR-Net,":[178],"upscale":[180],"predicted":[182],"its":[187],"counterpart.":[189],"Unlike":[190],"SR-Net":[194],"can":[195],"be":[196],"effectively":[197],"trained":[198],"only":[200,224],"limited":[202],"available":[207],"dataset.":[211],"Experimental":[212],"results":[213],"demonstrate":[214],"proposed":[217],"framework":[218],"achieves":[219],"compelling":[220],"reconstruction":[221],"performance":[222],"4%":[225],"dataset":[231],"having":[232],"labels,":[236],"significantly":[238],"reduces":[239],"data":[240],"overhead":[242],"facilitates":[244],"practical":[245],"deployment.":[246]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-03T00:00:00"}