{"id":"W2072637209","doi":"10.1109/icnnsp.2008.4590299","title":"A closed-form semi-blind solution to MIMO-OFDM channel estimation","year":2008,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Weighting; Channel (broadcasting); MIMO; Orthogonal frequency-division multiplexing; Computer science; Closed-form expression; MIMO-OFDM; Algorithm; Mean squared error; Computation; Mathematics; Statistics; Telecommunications","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":[0.0009245542,0.0008466556,0.0009822499,0.0004483729,0.0004687238,0.001124339,0.0007917262,0.001469121,0.003627399],"category_scores_gemma":[0.003330766,0.0004271047,0.0006090034,0.000574592,0.0008096818,0.001087247,0.001244883,0.001385927,0.001732158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003784237,"about_ca_system_score_gemma":0.00154661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009385998,"about_ca_topic_score_gemma":0.001482688,"domain_scores_codex":[0.999318,0.0001896775,0.00004299504,0.0001121805,0.000287585,0.00004958885],"domain_scores_gemma":[0.9988664,0.0005912149,0.00009982758,0.00009533321,0.0003176119,0.00002966269],"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":[0.0001692263,0.0001258569,0.0003296907,0.0007752047,0.00008719786,0.000197793,0.0002884985,0.5889804,0.03683211,0.07483529,0.006213733,0.2911651],"study_design_scores_gemma":[0.00002067676,0.00007048663,0.0001042073,0.00002637937,0.00001289143,0.0001820844,0.00002773735,0.9772093,0.007835815,0.01146464,0.00301864,0.00002725462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005298713,0.00004991514,0.9989377,0.00002489679,0.00001351593,0.0000126197,0.00001416988,0.0000956501,0.0003217952],"genre_scores_gemma":[0.1193681,0.0005800308,0.8745565,0.0001167779,0.000113955,0.0002369382,0.0001445237,0.00008186328,0.00480133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003627399,"threshold_uncertainty_score":0.01213485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03828096641204934,"score_gpt":0.2852023105465732,"score_spread":0.2469213441345239,"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."}}