{"id":"W2157604952","doi":"10.1109/icc.2002.996853","title":"A blind adaptive receiver for interference suppression and multipath reception in long-code DS-CDMA","year":2003,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code division multiple access; Minimum mean square error; Algorithm; Multipath propagation; Computer science; Multipath mitigation; Initialization; Least mean squares filter; Interference (communication); Mathematics; Channel (broadcasting); Control theory (sociology); Telecommunications; Adaptive filter; Statistics","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":[0.0006304693,0.0003963862,0.0003789483,0.0002760763,0.0002953961,0.0004516498,0.0005555488,0.0008040188,0.0009306077],"category_scores_gemma":[0.001435325,0.0002052823,0.0002660712,0.0001732703,0.0003683266,0.0006150561,0.0003294849,0.0005480217,0.0006070892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003484614,"about_ca_system_score_gemma":0.0004881074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005599431,"about_ca_topic_score_gemma":0.001158905,"domain_scores_codex":[0.9996124,0.0001167319,0.00001635528,0.00004894208,0.0001757444,0.00002984215],"domain_scores_gemma":[0.9995988,0.0001545559,0.00003456359,0.00004689575,0.0001466234,0.00001858197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006968814,0.0001708581,0.001409089,0.0002346144,0.0001070051,0.0001941481,0.000203244,0.09816848,0.386051,0.04615178,0.002321732,0.4642911],"study_design_scores_gemma":[0.00006697069,0.0003820882,0.0006358748,0.00001631758,0.00006665033,0.0004078971,0.00002059881,0.8859088,0.0990383,0.003676682,0.009725476,0.00005421445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01003371,0.0002643692,0.9883298,0.0000621635,0.0000506393,0.00001975564,0.000007376942,0.0004236703,0.000808469],"genre_scores_gemma":[0.3239699,0.0003756856,0.6690159,0.0002069868,0.0001181467,0.00006663815,0.00003712882,0.00004545151,0.006164162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009306077,"threshold_uncertainty_score":0.003334284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07211952345614664,"score_gpt":0.3342747441645485,"score_spread":0.2621552207084019,"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."}}