{"id":"W4409882855","doi":"10.1109/twc.2025.3562818","title":"Attention-Based Deep Learning for Hybrid Beamforming in OFDM Systems With Phase Noise","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; Orthogonal frequency-division multiplexing; Computer science; Phase noise; Noise (video); Telecommunications; Electronic engineering; Speech recognition; Artificial intelligence; Engineering; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004495084,0.0006578972,0.0004061036,0.000216003,0.0002488844,0.0004044755,0.0007616549,0.0006173183,0.002224847],"category_scores_gemma":[0.001192636,0.0002973146,0.0002746984,0.0003570243,0.0004071824,0.0008635852,0.0009266634,0.0009786339,0.0004531172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004584333,"about_ca_system_score_gemma":0.000633474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002561021,"about_ca_topic_score_gemma":0.005329005,"domain_scores_codex":[0.9998217,0.00004507542,0.000008364595,0.00003216958,0.0000567968,0.0000358918],"domain_scores_gemma":[0.9996718,0.0001878742,0.00003186406,0.0000216709,0.00006784246,0.00001902322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001160978,0.00008201341,0.0007271424,0.0001014912,0.0000536027,0.0001040503,0.00008389603,0.6964599,0.01866253,0.01793207,0.002645649,0.2630316],"study_design_scores_gemma":[0.000003428793,0.00001580117,0.0000334635,0.000002792893,0.000003405817,0.00001106953,0.000002914339,0.9964657,0.001195748,0.001993844,0.0002692474,0.000002500939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007425967,0.0001750609,0.9907482,0.0001242264,0.00001763919,0.000009964918,0.00002306551,0.0002208585,0.001255033],"genre_scores_gemma":[0.7201259,0.0003492158,0.2731894,0.0003825903,0.00009419425,0.00009581757,0.000147815,0.00007997128,0.005535135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002561021,"threshold_uncertainty_score":0.007442832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180724823746513,"score_gpt":0.2673917566413452,"score_spread":0.2455845084038801,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}