{"id":"W2133906719","doi":"10.1109/wcnc.2005.1424521","title":"Convergence behavior of iterative turbo multiuser detection algorithms","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"EXIT chart; Decoding methods; Algorithm; Multiuser detection; Turbo code; Computer science; Convergence (economics); Turbo; Information transfer; Turbo equalizer; Bit error rate; Chart; Code division multiple access; Mathematics; Concatenated error correction code; Telecommunications; Statistics; Engineering; Block code","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":[0.004653248,0.0007582794,0.0008851523,0.00169603,0.000659641,0.001328841,0.0008321449,0.0009949,0.001600077],"category_scores_gemma":[0.03135881,0.0004571313,0.0005780724,0.0007908771,0.001688422,0.001695124,0.001436616,0.001235104,0.0006758458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200755,"about_ca_system_score_gemma":0.001108224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001394179,"about_ca_topic_score_gemma":0.0005486113,"domain_scores_codex":[0.9977528,0.001101385,0.0001043223,0.000197159,0.0006594645,0.000184837],"domain_scores_gemma":[0.9821613,0.01188248,0.001231574,0.001110763,0.003278709,0.0003351762],"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":[0.0005039716,0.00004951304,0.003597847,0.0001489347,0.00007604698,0.0001936452,0.000564929,0.8351914,0.01044307,0.1044993,0.00088459,0.04384663],"study_design_scores_gemma":[0.000006632725,0.00005030073,0.0003384826,0.00001607834,0.000005363201,0.00006292378,0.00001953417,0.9867623,0.004980516,0.007515645,0.0002228726,0.00001928216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.158255,0.0006347659,0.8314028,0.0002434134,0.00003791345,0.00008648691,0.00008946756,0.0008201558,0.008430031],"genre_scores_gemma":[0.8902237,0.0004016409,0.1047024,0.00007757414,0.0000324805,0.00014283,0.0001840536,0.0002755503,0.003959795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004653248,"threshold_uncertainty_score":0.02460903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186806650072363,"score_gpt":0.2601349792801129,"score_spread":0.2482669127793892,"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."}}