{"id":"W2148921273","doi":"10.1109/glocom.2001.965513","title":"Multiuser interference cancellation aided adaptation of a MMSE receiver for direct-sequence code-division multiple-access systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Single antenna interference cancellation; Code division multiple access; Computer science; Least mean squares filter; Minimum mean square error; Interference (communication); Algorithm; Adaptive filter; Overhead (engineering); Convergence (economics); Recursive least squares filter; Code (set theory); Multiuser detection; Channel (broadcasting); Telecommunications; Mathematics; Statistics; Decoding methods","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005281233,0.0001579979,0.0002527949,0.0001948726,0.0001574204,0.0002463918,0.001939162,0.00009359486,0.00003588159],"category_scores_gemma":[0.0003095244,0.0001438263,0.00006405124,0.0006455486,0.00008117003,0.001231908,0.0004271774,0.0001438189,0.00002096971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132129,"about_ca_system_score_gemma":0.0000471455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001301949,"about_ca_topic_score_gemma":0.0008396811,"domain_scores_codex":[0.998032,0.0002663937,0.0005215728,0.0004486362,0.0004286205,0.0003027873],"domain_scores_gemma":[0.9966537,0.00115412,0.0002969989,0.001068252,0.0007299127,0.00009697308],"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":[0.0001059393,0.0003781045,0.007770702,0.0003273763,0.00006687058,0.000002620889,0.004610698,0.6218361,0.01304652,0.009989429,0.004977183,0.3368884],"study_design_scores_gemma":[0.00057344,0.00008429988,0.001045017,0.0001588319,0.000002687859,0.000001163311,0.00006076595,0.993561,0.002834815,0.00005401859,0.00146013,0.0001638654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02119151,0.0004414284,0.9750201,0.0003579575,0.0002682377,0.0009812033,0.00001514937,0.0001745803,0.001549798],"genre_scores_gemma":[0.9710749,0.000299083,0.02719689,0.00002756829,0.00002566431,0.0002030992,0.00001235552,0.000014746,0.001145713],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9498834,"threshold_uncertainty_score":0.5865066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2181073831458487,"score_gpt":0.3529902353833545,"score_spread":0.1348828522375058,"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."}}