{"id":"https://openalex.org/W4417246049","doi":"https://doi.org/10.1186/s13634-025-01276-x","title":"Hybrid deep learning for robust spectrum sensing over AWGN and fading channels","display_name":"Hybrid deep learning for robust spectrum sensing over AWGN and fading channels","publication_year":2025,"publication_date":"2025-12-11","ids":{"openalex":"https://openalex.org/W4417246049","doi":"https://doi.org/10.1186/s13634-025-01276-x"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-025-01276-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01276-x","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s13634-025-01276-x","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120774569","display_name":"Sarah Yahya Salih Al-Hajm","orcid":null},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Sarah Yahya Salih Al-Hajm","raw_affiliation_strings":["Graduate School of Natural and Applied Sciences, Erciyes University, 38039, Kayseri, Turkey"],"affiliations":[{"raw_affiliation_string":"Graduate School of Natural and Applied Sciences, Erciyes University, 38039, Kayseri, Turkey","institution_ids":["https://openalex.org/I87673952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038466164","display_name":"\u0130brahim Devel\u0131","orcid":"https://orcid.org/0000-0001-5878-677X"},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ibrahim Develi","raw_affiliation_strings":["Electrical and Electronics Engineering Department, Erciyes University, 38039, Kayseri, Turkey"],"affiliations":[{"raw_affiliation_string":"Electrical and Electronics Engineering Department, Erciyes University, 38039, Kayseri, Turkey","institution_ids":["https://openalex.org/I87673952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076402305","display_name":"Kenan Ko\u00e7kaya","orcid":"https://orcid.org/0000-0002-5253-1511"},"institutions":[{"id":"https://openalex.org/I12700597","display_name":"Sivas Cumhuriyet \u00dcniversitesi","ror":"https://ror.org/04f81fm77","country_code":"TR","type":"education","lineage":["https://openalex.org/I12700597"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Kenan Kockaya","raw_affiliation_strings":["Motor Vehicles and Transportation Department, Sivas Cumhuriyet University, 58140, Sivas, Turkey"],"affiliations":[{"raw_affiliation_string":"Motor Vehicles and Transportation Department, Sivas Cumhuriyet University, 58140, Sivas, Turkey","institution_ids":["https://openalex.org/I12700597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120774569"],"corresponding_institution_ids":["https://openalex.org/I87673952"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45582969,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2026","issue":"1","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":0.9480999708175659,"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":0.9480999708175659,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.026200000196695328,"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/T13553","display_name":"Age of Information Optimization","score":0.00279999990016222,"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/rician-fading","display_name":"Rician fading","score":0.9180999994277954},{"id":"https://openalex.org/keywords/fading","display_name":"Fading","score":0.7926999926567078},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.7297999858856201},{"id":"https://openalex.org/keywords/cognitive-radio","display_name":"Cognitive radio","score":0.6380000114440918},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5220999717712402},{"id":"https://openalex.org/keywords/bit-error-rate","display_name":"Bit error rate","score":0.4560000002384186},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.44690001010894775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4372999966144562}],"concepts":[{"id":"https://openalex.org/C60472773","wikidata":"https://www.wikidata.org/wiki/Q7331156","display_name":"Rician fading","level":4,"score":0.9180999994277954},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.7926999926567078},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.7297999858856201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084000110626221},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.6380000114440918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5250999927520752},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5220999717712402},{"id":"https://openalex.org/C56296756","wikidata":"https://www.wikidata.org/wiki/Q840922","display_name":"Bit error rate","level":3,"score":0.4560000002384186},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4372999966144562},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C56985126","wikidata":"https://www.wikidata.org/wiki/Q854039","display_name":"Rayleigh fading","level":4,"score":0.3711000084877014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34700000286102295},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.33899998664855957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C161126049","wikidata":"https://www.wikidata.org/wiki/Q5429266","display_name":"Fading distribution","level":5,"score":0.33410000801086426},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C105344744","wikidata":"https://www.wikidata.org/wiki/Q958957","display_name":"Spread spectrum","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.27570000290870667},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-025-01276-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01276-x","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f2789a9504e44bc48e25e702cd290a7f","is_oa":true,"landing_page_url":"https://doaj.org/article/f2789a9504e44bc48e25e702cd290a7f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2026, Iss 1, Pp 1-15 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-025-01276-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01276-x","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2071707134","https://openalex.org/W2079735306","https://openalex.org/W2101840010","https://openalex.org/W2144369278","https://openalex.org/W2164701487","https://openalex.org/W2885642955","https://openalex.org/W2939053413","https://openalex.org/W2971778960","https://openalex.org/W2973209754","https://openalex.org/W3010867338","https://openalex.org/W3027109963","https://openalex.org/W3035433808","https://openalex.org/W3039695658","https://openalex.org/W3099824014","https://openalex.org/W3100777112","https://openalex.org/W3126883438","https://openalex.org/W3139996145","https://openalex.org/W3152753009","https://openalex.org/W3211153404","https://openalex.org/W4206268350","https://openalex.org/W4288391355","https://openalex.org/W4313017643","https://openalex.org/W4319986857","https://openalex.org/W4323644251","https://openalex.org/W4385834069","https://openalex.org/W4387223140","https://openalex.org/W4388849796","https://openalex.org/W4389060691","https://openalex.org/W4391358705","https://openalex.org/W4393293576","https://openalex.org/W4396792350","https://openalex.org/W4400485124","https://openalex.org/W4401701784","https://openalex.org/W4405285183","https://openalex.org/W4405902977","https://openalex.org/W4406418910","https://openalex.org/W4408017223","https://openalex.org/W4408253515","https://openalex.org/W4408358748","https://openalex.org/W4409763379","https://openalex.org/W4413449360"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,23,46,81,93,112,127,133,153],"reliable":[2],"spectrum":[3,10],"sensing":[4],"(SS)":[5],"is":[6],"crucial":[7],"for":[8,155],"optimal":[9],"usage":[11],"in":[12,43],"cognitive":[13],"radio":[14],"(CR)":[15],"networks":[16],"under":[17,129,162],"challenging":[18],"low":[19],"signal-to-noise":[20],"ratio":[21],"(SNR)":[22],"fading":[24,45,114],"channel":[25,101,164],"conditions.":[26,165],"The":[27,49,116],"Bi-CLNetwork,":[28],"a":[29,72,156],"hybrid":[30,51,117],"deep":[31],"learning":[32],"(DL)":[33],"architecture,":[34],"was":[35,86],"proposed":[36,50],"to":[37,77,138],"enhance":[38],"the":[39,145],"performance":[40],"of":[41,123,147,160],"SS":[42],"both":[44],"non-fading":[47],"environments.":[48],"DL":[52,118,140],"framework":[53],"integrates":[54],"bidirectional":[55],"long":[56],"short-term":[57],"memory":[58],"(Bi-LSTM)":[59],"layers":[60],"with":[61],"two":[62],"parallel":[63],"one-dimensional":[64],"convolutional":[65],"neural":[66],"network-LSTM":[67],"(1D-CNN-LSTM)":[68],"branches,":[69],"accompanied":[70],"by":[71,149],"global":[73],"average":[74],"pooling":[75],"layer":[76],"reduce":[78],"parameter":[79],"complexity":[80],"strengthen":[82],"generalization.":[83],"Our":[84],"approach":[85],"assessed":[87],"using":[88],"bit":[89],"error":[90],"rate":[91],"(BER)":[92],"receiver":[94],"operating":[95],"characteristic":[96],"(ROC)":[97],"metrics":[98],"across":[99],"multiple":[100],"models,":[102],"such":[103],"as":[104],"additive":[105],"white":[106],"Gaussian":[107],"noise":[108],"(AWGN),":[109],"Nakagami-m,":[110,131],"Rayleigh,":[111,132],"Rician":[113,134],"channels.":[115],"model":[119],"achieves":[120],"reductions":[121],"BER":[122],"36.55%,":[124],"40.44%,":[125],"42.72%,":[126],"41.77%":[128],"AWGN,":[130],"channels,":[135],"respectively,":[136],"compared":[137],"existing":[139],"models.":[141],"Moreover,":[142],"it":[143],"increases":[144],"probability":[146,159],"detection":[148],"65.1%,":[150],"46.8%,":[151],"42.9%,":[152],"101.24%":[154],"false":[157],"alarm":[158],"0.1":[161],"identical":[163]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-12-11T00:00:00"}