{"id":"https://openalex.org/W7164896268","doi":"https://doi.org/10.1109/access.2026.3704322","title":"PRB-Level Channel Power Estimation Using Deep Learning for Robust CQI Reporting in 5G and Beyond","display_name":"PRB-Level Channel Power Estimation Using Deep Learning for Robust CQI Reporting in 5G and Beyond","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7164896268","doi":"https://doi.org/10.1109/access.2026.3704322"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3704322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3704322","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3704322","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039695589","display_name":"Bhaskar Banerjee","orcid":"https://orcid.org/0000-0002-8829-5792"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Bhaskar Banerjee","raw_affiliation_strings":["Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland"],"raw_orcid":"https://orcid.org/0000-0002-8829-5792","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034665130","display_name":"Zaheer Khan","orcid":"https://orcid.org/0000-0003-2951-5684"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Zaheer Khan","raw_affiliation_strings":["Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland"],"raw_orcid":"https://orcid.org/0000-0003-2951-5684","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland","institution_ids":["https://openalex.org/I98381234"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055603484","display_name":"Janne Lehtom\u00e4ki","orcid":"https://orcid.org/0000-0002-5081-1843"},"institutions":[{"id":"https://openalex.org/I98381234","display_name":"University of Oulu","ror":"https://ror.org/03yj89h83","country_code":"FI","type":"education","lineage":["https://openalex.org/I98381234"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Janne J. Lehtom\u00e4ki","raw_affiliation_strings":["Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland"],"raw_orcid":"https://orcid.org/0000-0002-5081-1843","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland","institution_ids":["https://openalex.org/I98381234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98381234"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7709753,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"96391","last_page":"96403"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.5116999745368958,"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":0.5116999745368958,"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/T11873","display_name":"PAPR reduction in OFDM","score":0.0689999982714653,"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/T10125","display_name":"Advanced Wireless Communication Techniques","score":0.03660000115633011,"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":0.7839000225067139},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.49390000104904175},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.42489999532699585},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4117000102996826},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.32600000500679016},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3172999918460846},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29919999837875366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7892000079154968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7839000225067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6158999800682068},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4002000093460083},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.32600000500679016},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3704322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3704322","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9a3c08f39c2a4d12b8a3baa3a1cf4a7b","is_oa":false,"landing_page_url":"https://doaj.org/article/9a3c08f39c2a4d12b8a3baa3a1cf4a7b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 96391-96403 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3704322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3704322","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"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],"channel":[1,56,74,78],"quality":[2,45],"indicator":[3],"(CQI)":[4],"reporting":[5,64,216],"is":[6,21,33,91],"essential":[7],"for":[8,128],"link":[9],"adaptation":[10],"in":[11,17],"5G":[12,139],"and":[13,23,53,105,115,132,154,171,179],"beyond":[14],"networks,":[15],"but":[16],"practical":[18],"receivers":[19],"it":[20],"inferred":[22],"strongly":[24],"affected":[25],"by":[26,71],"channel-estimation":[27],"quality.":[28],"While":[29],"least-squares":[30],"(LS)":[31],"estimation":[32,165],"computationally":[34],"efficient,":[35],"achieving":[36],"minimum":[37],"mean-square":[38],"error":[39],"(MMSE)-level":[40],"physical":[41],"resource":[42],"block":[43],"(PRB)/subband":[44],"typically":[46],"requires":[47],"refinement":[48,70],"steps":[49],"that":[50,66,204],"are":[51],"heuristic":[52],"sensitive":[54],"to":[55,177],"conditions.":[57],"This":[58],"paper":[59],"proposes":[60],"a":[61,122,189],"task-driven":[62],"CQI":[63,116,174,209,215],"branch":[65],"avoids":[67],"explicit":[68],"MMSE-style":[69],"learning":[72,199],"PRB-level":[73],"power":[75],"from":[76],"LS-based":[77],"state":[79],"information":[80],"reference":[81],"signal":[82],"(CSI-RS)":[83],"estimates.":[84],"A":[85],"lightweight":[86],"convolutional":[87],"neural":[88],"network":[89],"(CNN)":[90],"trained":[92],"under":[93],"model-guided":[94],"supervision":[95],"using":[96,194],"linear":[97],"MMSE":[98],"(LMMSE)-derived":[99],"PRB-power":[100,164],"labels,":[101],"enabling":[102],"implicit":[103],"denoising":[104],"interpolation":[106],"while":[107,211],"preserving":[108],"the":[109,160],"standard":[110],"signal-to-interference-plus-noise":[111],"ratio":[112,157],"(SINR)":[113],"aggregation":[114],"mapping":[117],"flow.":[118],"The":[119],"model":[120],"uses":[121],"compact":[123],"multi-channel":[124],"feature":[125],"representation":[126],"designed":[127],"sparse":[129],"CSI-RS":[130],"patches":[131],"8-bit":[133],"integer":[134],"(INT8)-friendly":[135],"deployment.":[136],"Across":[137],"3GPP-compliant":[138],"New":[140],"Radio":[141],"(NR)":[142],"link-level":[143],"simulations":[144],"spanning":[145],"tapped":[146],"delay":[147],"line":[148],"(TDL)":[149],"profiles,":[150],"broad":[151],"delay/Doppler":[152],"conditions,":[153],"wide":[155],"signal-to-noise":[156],"(SNR)":[158],"ranges,":[159],"proposed":[161],"method":[162],"improves":[163],"accuracy,":[166],"reduces":[167],"wideband":[168],"SINR":[169],"error,":[170],"mitigates":[172],"low-SNR":[173],"overestimation":[175],"relative":[176],"LS":[178],"smoothed-LS":[180],"baselines,":[181],"approaching":[182],"LMMSE-reference":[183],"behavior.":[184],"An":[185],"embedded":[186],"implementation":[187],"on":[188],"Zynq":[190],"UltraScale+":[191],"MPSoC":[192],"(ZCU102)":[193],"an":[195],"INT8":[196],"Xilinx":[197],"deep":[198],"processor":[200],"unit":[201],"(DPU)":[202],"shows":[203],"hardware":[205],"inference":[206],"preserves":[207],"FP32-level":[208],"fidelity":[210],"meeting":[212],"representative":[213],"real-time":[214],"intervals.":[217]},"counts_by_year":[],"updated_date":"2026-07-03T06:15:21.484131","created_date":"2026-06-17T00:00:00"}