{"id":"W2127820012","doi":"10.1109/icc.2005.1494705","title":"A subspace-based iterative group blind multiuser detection and decoding for coded CDMA systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Code division multiple access; Multiuser detection; Decoding methods; Single antenna interference cancellation; Computer science; Turbo; Telecommunications link; Interference (communication); Subspace topology; Minimum mean square error; Algorithm; Bit error rate; Channel (broadcasting); Turbo code; Electronic engineering; Telecommunications; Mathematics; Artificial intelligence; Engineering; 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":[0.000833245,0.0006316945,0.0007410062,0.0005308962,0.0005575774,0.0006034968,0.0008009788,0.00106089,0.0008392704],"category_scores_gemma":[0.002265075,0.0002903753,0.00049489,0.000625344,0.0006975101,0.0008685022,0.0006971765,0.0007495825,0.0008872008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003681463,"about_ca_system_score_gemma":0.0011478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001461678,"about_ca_topic_score_gemma":0.001884742,"domain_scores_codex":[0.9990966,0.0003426037,0.0000430542,0.00007077469,0.0003740262,0.00007297621],"domain_scores_gemma":[0.9988515,0.0003667876,0.0001153904,0.0001578097,0.0004554006,0.00005299373],"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":[0.0005316877,0.0001420789,0.001710218,0.0002939064,0.0001614054,0.000322299,0.0003631888,0.3746752,0.09520655,0.04545478,0.003639136,0.4774995],"study_design_scores_gemma":[0.00002061579,0.0001389352,0.000149185,0.000007229663,0.00001667425,0.0001844988,0.00001154651,0.9798135,0.01495157,0.00303021,0.001650491,0.000025438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006769683,0.0001886395,0.9921483,0.00004814913,0.00002584631,0.00001831153,0.00001352963,0.0003055868,0.0004819251],"genre_scores_gemma":[0.3215719,0.0004259236,0.67501,0.0001160569,0.0000679318,0.0001217656,0.00009745472,0.00005320746,0.002535761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001461678,"threshold_uncertainty_score":0.004406691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04793389280293045,"score_gpt":0.3124258065857712,"score_spread":0.2644919137828408,"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."}}