{"id":"W2169617207","doi":"10.1109/vetecs.2005.1543299","title":"Adaptive Multistage Detection for DS-CDMA Systems in Multipath Fading Channels","year":2005,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Fading; Code division multiple access; Multiuser detection; Multipath propagation; Computer science; Detector; Single antenna interference cancellation; Spread spectrum; Algorithm; Upper and lower bounds; Interference (communication); Electronic engineering; Fading distribution; Telecommunications; Mathematics; Channel (broadcasting); Rayleigh fading; Decoding methods; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004819119,0.0004466361,0.0002681669,0.000299181,0.00029573,0.0004014906,0.0007564905,0.0005033665,0.0006756738],"category_scores_gemma":[0.001348326,0.0002741398,0.0002936622,0.0002392661,0.0003297054,0.0005583397,0.0005059002,0.0004828992,0.0002809021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003135836,"about_ca_system_score_gemma":0.0004961024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008324665,"about_ca_topic_score_gemma":0.002070703,"domain_scores_codex":[0.9995784,0.000102019,0.00001827739,0.0000474502,0.0002104067,0.00004349071],"domain_scores_gemma":[0.9994648,0.0002298978,0.00006799059,0.00006757906,0.0001470364,0.00002262709],"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":[0.0006233557,0.0001941194,0.004795638,0.0003379845,0.0001474757,0.000343287,0.000306142,0.266904,0.322146,0.0276645,0.001349944,0.3751876],"study_design_scores_gemma":[0.00002348079,0.0002712837,0.0007621841,0.000008389744,0.00003175098,0.0001781773,0.00001068288,0.9604834,0.03411254,0.00268167,0.001411268,0.00002522062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0404937,0.0004077272,0.9578878,0.00006425423,0.00002329261,0.00004297319,0.00001707104,0.0002476748,0.0008154509],"genre_scores_gemma":[0.6811525,0.0003962487,0.3161819,0.00008671313,0.00004447099,0.00007001387,0.00004414233,0.00001625974,0.002007728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008324665,"threshold_uncertainty_score":0.002548635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05828577708386392,"score_gpt":0.311112162934333,"score_spread":0.2528263858504691,"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."}}