{"id":"W4388126712","doi":"10.3390/electronics12214501","title":"Robust Adaptive Beamforming for Interference Suppression Based on SNR","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China; Qingdao Agricultural University","keywords":"Adaptive beamformer; Covariance matrix; Beamforming; Interference (communication); Signal-to-interference-plus-noise ratio; Algorithm; Control theory (sociology); Noise (video); Noise power; Subspace topology; Zero-forcing precoding; Quadratic programming; Computer science; Mathematics; Power (physics); Mathematical optimization; Telecommunications; MIMO; Precoding; Artificial intelligence","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":[0.0003075356,0.0000990719,0.0001186069,0.0002007134,0.00008610631,0.0000361171,0.0004769683,0.00005206267,0.000005510345],"category_scores_gemma":[0.0001416207,0.0000938413,0.00005956017,0.0005222234,0.00001899472,0.00024617,0.0000751715,0.0001174103,0.00001339065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103391,"about_ca_system_score_gemma":0.0001464225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003391271,"about_ca_topic_score_gemma":0.00000581174,"domain_scores_codex":[0.9991004,0.00002817083,0.0001739922,0.0002457845,0.0001844537,0.0002671716],"domain_scores_gemma":[0.9991748,0.0002621901,0.0001019751,0.0003185878,0.0001075039,0.00003490578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002187349,0.0002561126,0.00007551283,0.0001299136,0.0000391363,0.000003372203,0.0008398799,0.1101383,0.02269308,0.3066183,0.02398082,0.5350068],"study_design_scores_gemma":[0.000118906,0.0005315827,0.0000305833,0.00006580542,0.000002373117,5.848659e-7,0.000005149368,0.7669482,0.2223458,0.007162953,0.002701763,0.00008634659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001364918,0.00003027094,0.9962065,0.0003014969,0.0001700512,0.0002524073,0.000004713141,0.0005965712,0.001073063],"genre_scores_gemma":[0.7005448,0.0000296228,0.2988076,0.0001356608,0.00002968423,0.000139046,0.0000180476,0.00001895931,0.0002765695],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6991799,"threshold_uncertainty_score":0.3826737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04036966231173403,"score_gpt":0.2828123081585482,"score_spread":0.2424426458468142,"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."}}