{"id":"W2153875891","doi":"10.1109/lcomm.2005.1496590","title":"CRLBs for NDA ML estimation of UWB channels","year":2005,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Computer science; Bandwidth (computing); Maximum likelihood; Channel (broadcasting); Algorithm; Cramér–Rao bound; Estimation theory; Statistics; Electronic engineering; Telecommunications; Mathematics; Engineering","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":[0.003863057,0.001864001,0.00121726,0.002725572,0.0006472105,0.002816654,0.001650253,0.001562947,0.007325721],"category_scores_gemma":[0.04658754,0.001005015,0.0009615215,0.002090602,0.001395513,0.002303529,0.002496863,0.003323729,0.004803583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001494543,"about_ca_system_score_gemma":0.001812031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002199448,"about_ca_topic_score_gemma":0.003171416,"domain_scores_codex":[0.9959222,0.001075194,0.0002678092,0.0004878702,0.002016088,0.0002308493],"domain_scores_gemma":[0.9813525,0.01319339,0.0009850311,0.001820721,0.002468128,0.0001802104],"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":[0.0003379594,0.0001334627,0.0008787377,0.0009802667,0.0001347326,0.0001796927,0.0003261025,0.4325394,0.02314596,0.1861071,0.009937798,0.3452987],"study_design_scores_gemma":[0.00003148105,0.0000909818,0.0007134426,0.0003173999,0.00004606373,0.0003503497,0.00006701664,0.8999546,0.01413819,0.07152778,0.01267772,0.00008490308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001408368,0.001349952,0.993919,0.000137898,0.00009959513,0.00002623105,0.0001595028,0.0005988186,0.002300579],"genre_scores_gemma":[0.1837333,0.005251367,0.7999439,0.0004710926,0.0006238865,0.0006177487,0.001019832,0.0008359072,0.007503092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007325721,"threshold_uncertainty_score":0.02450693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145362736361066,"score_gpt":0.2612689475881634,"score_spread":0.2398153202245528,"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."}}