{"id":"W2105224268","doi":"10.1109/lcomm.2005.1576581","title":"Maximum likelihood estimation of the K factor in ricean fading channels","year":2005,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fading; Weibull fading; Fading distribution; Estimator; Channel state information; Envelope (radar); Channel (broadcasting); Statistics; Mathematics; Algorithm; Computer science; Telecommunications; Rayleigh fading; Wireless","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":[0.001576359,0.0008853201,0.0009136465,0.0005159019,0.0003266986,0.0008914775,0.0006271035,0.0007413236,0.0008147319],"category_scores_gemma":[0.01167232,0.0005035141,0.0003814354,0.0008488917,0.00103774,0.002119286,0.0007674409,0.0006803546,0.0005574402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000447581,"about_ca_system_score_gemma":0.0006674382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001231424,"about_ca_topic_score_gemma":0.0009641126,"domain_scores_codex":[0.9993305,0.0003230237,0.00002880916,0.00009696063,0.0001607035,0.00005997672],"domain_scores_gemma":[0.996527,0.002708297,0.0002983006,0.0001666755,0.0002694176,0.00003034181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002877557,0.00003261196,0.001769353,0.0002096136,0.00006350206,0.0001842506,0.000134754,0.8477005,0.009010232,0.02920388,0.0008479858,0.1105554],"study_design_scores_gemma":[0.00001605207,0.00002742834,0.0003654926,0.00001528116,0.000009564109,0.00008228679,0.00001400979,0.9836145,0.002668761,0.01270821,0.0004559608,0.00002234615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01098139,0.0004181853,0.9879299,0.00005469813,0.000007352275,0.000009167802,0.00002343287,0.0001226807,0.0004531025],"genre_scores_gemma":[0.6556817,0.002517662,0.3384339,0.00007988472,0.00009437493,0.00008609636,0.0003042946,0.0001128267,0.002689261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001576359,"threshold_uncertainty_score":0.008336723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205907251746842,"score_gpt":0.2648693817319885,"score_spread":0.2442786565573043,"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."}}