{"id":"W3166653712","doi":"10.1109/jsac.2021.3087234","title":"Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment","year":2021,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Beamforming; Fading; Adaptive beamformer; Channel state information; Channel (broadcasting); Artificial neural network; Algorithm; Artificial intelligence; Telecommunications; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002749895,0.0001614932,0.0002093842,0.00026274,0.0003655061,0.00007882247,0.000339264,0.00008259893,0.00001193093],"category_scores_gemma":[0.0002238673,0.0001759284,0.00007841101,0.0005218773,0.00001822798,0.0001513685,0.00005567309,0.0008424923,0.000008542538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000406661,"about_ca_system_score_gemma":0.0001043606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003396832,"about_ca_topic_score_gemma":0.00004654226,"domain_scores_codex":[0.9987701,0.0001845416,0.0004118479,0.0001540496,0.0001885315,0.0002909959],"domain_scores_gemma":[0.9986581,0.0003052387,0.00008420537,0.0003794324,0.0004296604,0.0001433487],"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":[0.0000584973,0.0002063733,0.00001243773,0.00001226969,0.0001316946,0.000002881919,0.003701348,0.9519389,0.01924255,0.0004271341,0.0001397657,0.02412617],"study_design_scores_gemma":[0.0007840634,0.0001346532,0.00006851603,0.0001569512,0.00003482466,0.0001043238,0.001810903,0.9340141,0.05931275,0.0005441362,0.002739566,0.0002951945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02678687,0.0002050989,0.9631607,0.0002232931,0.0001731404,0.0003209482,0.000009624355,0.0000885277,0.009031782],"genre_scores_gemma":[0.8915582,0.0002942962,0.1076043,0.0001696132,0.0001082267,0.0001283545,0.0000544425,0.00003990603,0.00004262713],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8647713,"threshold_uncertainty_score":0.7174152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06350940356307683,"score_gpt":0.2981327057581407,"score_spread":0.2346233021950638,"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."}}