{"id":"W2128369455","doi":"10.1109/vetecs.2007.538","title":"Convergence Speed of Iterative Multi-user Detection for Turbo-Coded CDMA","year":2007,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Convergence (economics); Computer science; Turbo; Speedup; Algorithm; Turbo code; Turbo equalizer; Decoding methods; Iterative method; Code division multiple access; Interference (communication); Iterative and incremental development; Channel (broadcasting); Theoretical computer science; Telecommunications; Low-density parity-check code; Parallel computing; Engineering","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":[0.0007914716,0.00008848936,0.0001286981,0.0001284148,0.0001017715,0.00004891367,0.001003825,0.0000633688,0.00002788021],"category_scores_gemma":[0.0001026204,0.00007847996,0.0000602159,0.0005042477,0.00007032454,0.0003620172,0.0002646958,0.0001208082,0.0000200467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004585737,"about_ca_system_score_gemma":0.00003750443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004951505,"about_ca_topic_score_gemma":0.0002250106,"domain_scores_codex":[0.9989007,0.0000728243,0.0002887053,0.0002343861,0.0002398625,0.0002635519],"domain_scores_gemma":[0.9981948,0.0004797493,0.00009970375,0.000707447,0.0004404915,0.00007776787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004186171,0.000864219,0.004535753,0.0001366972,0.0001698958,0.000007386484,0.006735222,0.007029287,0.4661779,0.1546095,0.002082101,0.3572334],"study_design_scores_gemma":[0.0004965561,0.00007129938,0.003555912,0.000009209985,0.000001214146,0.000001638111,0.00004394846,0.5480219,0.4463893,0.0002199951,0.001098143,0.0000909908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03210836,0.00004771385,0.9663084,0.0002090018,0.0001652656,0.000386725,8.501699e-7,0.00008376982,0.0006899279],"genre_scores_gemma":[0.8705339,0.00001303854,0.1274773,0.00006955813,0.00002604882,0.00001188246,0.00000158477,0.00000642359,0.001860159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8388311,"threshold_uncertainty_score":0.320032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04828721281306724,"score_gpt":0.3381590687214145,"score_spread":0.2898718559083473,"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."}}