{"id":"W2964200041","doi":"10.1109/access.2019.2930029","title":"An Adaptive Approach for the Joint Antenna Selection and Beamforming Optimization","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nanjing University; Nanjing University of Posts and Telecommunications; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Concordia University","keywords":"Beamforming; Computer science; Adaptive beamformer; Smart antenna; Telecommunications link; Antenna (radio); Minimum mean square error; Context (archaeology); Computational complexity theory; Optimization problem; Antenna array; Selection (genetic algorithm); Computer engineering; Mathematical optimization; Electronic engineering; Algorithm; Telecommunications; Directional antenna; Mathematics; Engineering; Artificial intelligence; Estimator","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":[0.0007204044,0.001041201,0.0006060549,0.0004489156,0.0002429123,0.0006278405,0.001025787,0.0009766415,0.002482663],"category_scores_gemma":[0.001607953,0.0003955399,0.0006380721,0.000725059,0.000696585,0.0007446092,0.001081977,0.001369369,0.0008094191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000363632,"about_ca_system_score_gemma":0.0007895074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179841,"about_ca_topic_score_gemma":0.001512397,"domain_scores_codex":[0.9995043,0.0001893196,0.00002191851,0.00009739291,0.0001520157,0.00003497932],"domain_scores_gemma":[0.9996049,0.0002294509,0.00004179645,0.00003204684,0.00007615434,0.00001562609],"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":[0.00005798863,0.00004922407,0.0003390952,0.0001292614,0.00007235807,0.00006403867,0.00008764442,0.7795652,0.009096507,0.05902025,0.002907447,0.148611],"study_design_scores_gemma":[0.000008352254,0.00003807214,0.00005227906,0.000005912646,0.000007286966,0.00003498361,0.000006133082,0.9914771,0.000667517,0.00556822,0.002126463,0.00000764717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004315893,0.00006456042,0.9988089,0.00003841379,0.00001364891,0.000008508247,0.00000487743,0.00002658231,0.0006028989],"genre_scores_gemma":[0.1028506,0.000713244,0.8897608,0.0001984814,0.0001960804,0.0003431455,0.00008100219,0.00007266019,0.005783944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002482663,"threshold_uncertainty_score":0.008305311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02674035665744272,"score_gpt":0.2410675711564352,"score_spread":0.2143272144989925,"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."}}