{"id":"W2149233205","doi":"10.1109/tvt.2009.2039008","title":"Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems With Reduced Time Averaging","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Orthogonal frequency-division multiplexing; MIMO; Signal subspace; MIMO-OFDM; Subspace topology; Algorithm; Channel (broadcasting); Computer science; Wideband; Orthogonality; Linear subspace; Spatial correlation; Mathematics; Noise (video); Electronic engineering; Telecommunications; Engineering; 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":[0.0003824164,0.0002506723,0.0002714622,0.0009485509,0.000315503,0.0001568796,0.0006382109,0.0004458007,0.000006347877],"category_scores_gemma":[0.00001473768,0.0002341289,0.0000945911,0.0009065868,0.0001324717,0.000338217,0.000003113135,0.0006240008,0.00004700187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005906593,"about_ca_system_score_gemma":0.0001531484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001462941,"about_ca_topic_score_gemma":0.00002212579,"domain_scores_codex":[0.9984872,0.0000550281,0.0002833598,0.0005698708,0.0002581091,0.0003464847],"domain_scores_gemma":[0.9984692,0.0001049758,0.000160104,0.0009352556,0.0002551433,0.00007526475],"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":[0.0002718251,0.0009730869,0.000006569819,0.0001353122,0.0002192783,0.00003732639,0.0006724973,0.5472558,0.3834825,0.0297445,0.0004295465,0.03677177],"study_design_scores_gemma":[0.0007777525,0.0003016721,0.00000267016,0.000039161,0.00002065698,0.00005380468,0.00001416386,0.6459497,0.3513214,0.0007618995,0.0005414418,0.0002156906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06383027,0.00001327856,0.9281642,0.004539946,0.0003397031,0.001030019,0.000008627931,0.00201227,0.00006167104],"genre_scores_gemma":[0.8736097,0.000001663701,0.1253425,0.0001535718,0.00001844607,0.0006671487,0.000006534735,0.00003303748,0.0001674262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8097795,"threshold_uncertainty_score":0.9547498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075827012736612,"score_gpt":0.2419738827755609,"score_spread":0.2312156126481948,"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."}}