{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001005558,0.000133509,0.0001481002,0.00006569472,0.00009418619,0.00003484901,0.00005387116,0.0000562152,0.00006049289],"category_scores_gemma":[0.00004248981,0.0001378353,0.00007095668,0.0001057246,0.000005793499,0.0001059195,0.00004342522,0.0001230818,0.00002092192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022124,"about_ca_system_score_gemma":0.00001973009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003910023,"about_ca_topic_score_gemma":0.000006577568,"domain_scores_codex":[0.9992087,0.00001769511,0.0001795453,0.0002122695,0.0001271201,0.0002546659],"domain_scores_gemma":[0.9996412,0.00004600603,0.00001718107,0.00009629504,0.000095258,0.0001040125],"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":[0.00002500893,0.00002805753,0.000001171998,0.00003762423,0.00008871294,0.000001764769,0.002860968,0.9190934,0.02886201,0.0009387547,0.00009255528,0.04797001],"study_design_scores_gemma":[0.0002531969,0.00004039748,0.000001641221,0.00001126732,0.00001405326,0.00000515649,0.002688432,0.7058316,0.2893408,0.0001383186,0.00151455,0.0001605934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00877829,0.00003754933,0.9376404,0.00002368796,0.0001331532,0.0002886663,0.000003886939,0.0001666174,0.05292771],"genre_scores_gemma":[0.791933,0.000007559514,0.2071531,0.0001696553,0.000108264,0.0001189891,0.00004046455,0.00003297518,0.0004360121],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7831547,"threshold_uncertainty_score":0.562076,"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."}}