{"id":"W2142891162","doi":"10.1109/vetecf.2003.1285214","title":"Generalized blind subspace channel estimation","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Estimator; Subspace topology; Mean squared error; Algorithm; Kernel (algebra); Channel (broadcasting); Mathematics; Covariance; Signal subspace; Computer science; Telecommunications link; Minimum mean square error; Cramér–Rao bound; Mathematical optimization; Statistics; Artificial intelligence; Telecommunications; Combinatorics","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":[0.001013773,0.0007521528,0.001169827,0.000781177,0.0003176181,0.0008145847,0.0007770598,0.0009929319,0.001312368],"category_scores_gemma":[0.00331065,0.0003096939,0.0007266759,0.001345081,0.0009476601,0.001488054,0.001156519,0.0008776092,0.0006148226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003908745,"about_ca_system_score_gemma":0.001007439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001374922,"about_ca_topic_score_gemma":0.001122514,"domain_scores_codex":[0.9990242,0.0004218344,0.00003861275,0.0001318402,0.0003156466,0.00006802136],"domain_scores_gemma":[0.9989859,0.0003887423,0.0001067145,0.000236451,0.0002581914,0.00002390424],"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":[0.00008166079,0.00004001785,0.0004288274,0.0004318451,0.0001929238,0.00008114578,0.000106345,0.609993,0.0145082,0.1556139,0.002500321,0.2160217],"study_design_scores_gemma":[0.000008728099,0.00004208774,0.0001502636,0.00001997508,0.00002195385,0.00014061,0.00001150705,0.9627441,0.0045013,0.0281931,0.004133269,0.00003304405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001635254,0.0004562991,0.9972265,0.00003370761,0.00002056904,0.00001027458,0.00001955809,0.0000797146,0.0005180093],"genre_scores_gemma":[0.1882339,0.003401364,0.8039225,0.0001954654,0.0002611571,0.0001582148,0.0002203824,0.0001149393,0.003491973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001374922,"threshold_uncertainty_score":0.005361378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981746247295304,"score_gpt":0.2884505462002938,"score_spread":0.2586330837273407,"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."}}