{"id":"W2296426763","doi":"10.1109/cjece.2015.2436054","title":"Feasible Generalized Least Squares Estimation of Channel and Noise Covariance Matrices for MIMO Systems","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Covariance matrix; Mathematics; Estimator; Estimation of covariance matrices; Cramér–Rao bound; Covariance; Algorithm; Noise (video); Least-squares function approximation; Statistics; Estimation theory; Applied mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000175147,0.00008302733,0.0002133726,0.0003445537,0.00003389022,0.00006003355,0.0001805674,0.00003958119,4.275138e-7],"category_scores_gemma":[0.0000735021,0.00006409986,0.00003730117,0.0002381675,0.0000185709,0.0003097276,0.00001284152,0.0000434553,8.698407e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000040684,"about_ca_system_score_gemma":0.0001207088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002107728,"about_ca_topic_score_gemma":0.00001124847,"domain_scores_codex":[0.9993448,0.00001530033,0.0003018242,0.00009956511,0.00009186381,0.0001466905],"domain_scores_gemma":[0.9992244,0.000159131,0.0001763034,0.00007745478,0.0001782284,0.0001844642],"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":[0.00006705201,0.00006946532,0.001353214,0.001061432,0.0002367549,0.00003556815,0.0006583576,0.2310694,0.01295396,0.2815774,0.002134413,0.468783],"study_design_scores_gemma":[0.0003298706,0.0002443627,0.001353946,0.000248179,0.000009583682,0.0001148564,6.700246e-7,0.9924281,0.00413076,0.0008077765,0.0002409393,0.00009099634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02876854,0.001194674,0.9695334,0.0001737002,0.000203446,0.00009968501,0.000003829007,0.00001804221,0.000004627984],"genre_scores_gemma":[0.8766433,0.00004243273,0.123224,0.00001005426,0.00006278393,0.000004310728,2.076313e-7,0.000005627904,0.000007293105],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8478748,"threshold_uncertainty_score":0.2613916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009729116961973728,"score_gpt":0.206502392025653,"score_spread":0.1967732750636793,"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."}}