{"id":"W3160923511","doi":"10.1109/icassp39728.2021.9414523","title":"Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment","year":2021,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Beamforming; Computer science; Adaptive beamformer; Fading; Algorithm; Channel state information; Path (computing); Artificial neural network; Angle of arrival; Posterior probability; Sequence (biology); Artificial intelligence; Telecommunications; Antenna (radio); Wireless; Computer network; Decoding methods","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006347204,0.0006999917,0.0005778557,0.0003857921,0.0002355979,0.0005836819,0.001199054,0.0008866067,0.002018531],"category_scores_gemma":[0.001421427,0.0004183346,0.0004490519,0.0004758244,0.000588228,0.001047438,0.0008304869,0.001687919,0.0003743626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006509902,"about_ca_system_score_gemma":0.0007646307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002933321,"about_ca_topic_score_gemma":0.004432058,"domain_scores_codex":[0.9997551,0.00006667429,0.00001402186,0.00005248309,0.0000757304,0.00003601779],"domain_scores_gemma":[0.9995142,0.0002718301,0.00004601683,0.00003196632,0.0001137085,0.00002236415],"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":[0.00006707975,0.00005174986,0.0004826333,0.00007787489,0.00004273459,0.00004603694,0.00005238669,0.8349023,0.006081001,0.03030041,0.001373173,0.1265226],"study_design_scores_gemma":[0.000001881922,0.000007932568,0.00002288292,0.00000229934,0.000002057301,0.000004718061,0.000002138149,0.995954,0.0005584859,0.003135256,0.0003062948,0.000001965721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001787213,0.00009813779,0.9973004,0.00006626386,0.00001250553,0.000006027932,0.00001548028,0.0000742581,0.0006397719],"genre_scores_gemma":[0.5511135,0.0007935333,0.438695,0.0003651381,0.0001364705,0.0001718185,0.0002020639,0.00008903877,0.008433327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002933321,"threshold_uncertainty_score":0.00675267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03861452129401993,"score_gpt":0.2511525125898434,"score_spread":0.2125379912958235,"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."}}