{"id":"W2119113247","doi":"10.1109/icc.2001.937357","title":"Iterative multi-user turbo-code receiver for DS-CDMA","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Turbo code; Computer science; Code division multiple access; Turbo; Single antenna interference cancellation; Interference (communication); Decoding methods; Turbo equalizer; Serial concatenated convolutional codes; Computer engineering; Algorithm; Concatenated error correction code; Reduction (mathematics); Electronic engineering; Theoretical computer science; Computer network; Block code; Channel (broadcasting); Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007121711,0.0005402126,0.0004232111,0.0004161899,0.0003725157,0.0005828798,0.0007453397,0.0007741625,0.00225117],"category_scores_gemma":[0.002267177,0.000224179,0.0004685411,0.000518642,0.0004454848,0.000663194,0.0004940766,0.0009408139,0.001503856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004688424,"about_ca_system_score_gemma":0.00102504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001209639,"about_ca_topic_score_gemma":0.001903039,"domain_scores_codex":[0.9994546,0.0001635884,0.00002172951,0.00003180331,0.0002956508,0.00003277039],"domain_scores_gemma":[0.9990319,0.000345677,0.00008565797,0.0001303548,0.0003771655,0.00002923254],"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":[0.0002835719,0.0001182594,0.001268637,0.00046721,0.0001754679,0.0004399717,0.0004039246,0.448667,0.07900766,0.1459675,0.005539478,0.3176613],"study_design_scores_gemma":[0.00001875368,0.0001396597,0.0001848466,0.00002207383,0.00003590547,0.0003695439,0.00001234637,0.9629646,0.02154401,0.007384502,0.007282978,0.0000407203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006125211,0.0006382017,0.9895476,0.0001091084,0.00007436565,0.00003812583,0.00002298467,0.000229771,0.003214544],"genre_scores_gemma":[0.2483945,0.001314339,0.7402121,0.0001638184,0.0001424953,0.0001190661,0.00009233477,0.00006393001,0.009497506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00225117,"threshold_uncertainty_score":0.007530928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03974944268216022,"score_gpt":0.2760045241065892,"score_spread":0.236255081424429,"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."}}