{"id":"W2159180717","doi":"10.1109/icc.2003.1204401","title":"A widely linear LMS algorithm for MAI suppression for DS-CDMA","year":2004,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Least mean squares filter; Code division multiple access; Algorithm; Spread spectrum; Adaptive filter; Convergence (economics); Computer science; Minimum mean square error; Interference (communication); Computational complexity theory; Mean squared error; Mathematics; Telecommunications; Statistics; Channel (broadcasting)","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":[0.0005838288,0.0006967491,0.0004125164,0.000381847,0.0002788986,0.0004066584,0.0005748793,0.000553123,0.001601469],"category_scores_gemma":[0.002225427,0.0002202655,0.0002842473,0.0003962578,0.0004179661,0.0007337308,0.0004127338,0.0008161382,0.001234758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003665731,"about_ca_system_score_gemma":0.0005609831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008779818,"about_ca_topic_score_gemma":0.001371798,"domain_scores_codex":[0.9996005,0.0001236572,0.00002247693,0.00008994991,0.0001447349,0.00001865876],"domain_scores_gemma":[0.9995844,0.0001928302,0.00003753219,0.00003299232,0.000141329,0.00001089335],"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":[0.0001848868,0.00006142618,0.0007676135,0.0002970531,0.00008835701,0.0001317609,0.0001699827,0.1883546,0.06570696,0.04537951,0.00360268,0.6952552],"study_design_scores_gemma":[0.0000289712,0.0001055117,0.000266742,0.00002057391,0.00001917908,0.0001844629,0.00001941178,0.9658581,0.01691674,0.005675065,0.01087691,0.00002830778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00151222,0.0002404512,0.9974048,0.00005405415,0.00003747869,0.00001545766,0.000007820655,0.000193589,0.0005341671],"genre_scores_gemma":[0.134301,0.0008580873,0.8589606,0.0001614706,0.0001168267,0.0001783165,0.0000925488,0.0001242483,0.005206789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001601469,"threshold_uncertainty_score":0.005357385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04426920962336698,"score_gpt":0.3374070317458693,"score_spread":0.2931378221225023,"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."}}