{"id":"W2165026207","doi":"10.1109/twc.2003.821218","title":"Turbo Multiuser Detection for Differentially Modulated CDMA","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Turbo code; Multiuser detection; Decoding methods; Turbo; Algorithm; Differential coding; Detector; Code division multiple access; Turbo equalizer; Modulation (music); Encoder; Electronic engineering; Telecommunications; Concatenated error correction code; Physics; Block code; Engineering; Acoustics","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":[0.000712808,0.0003442166,0.000377034,0.0003294528,0.0002778309,0.0005466674,0.000445728,0.0006989284,0.0007868456],"category_scores_gemma":[0.004404652,0.0001835902,0.0003259419,0.0004763018,0.0006341569,0.0006320688,0.0004933032,0.0005042484,0.0003283112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006062597,"about_ca_system_score_gemma":0.0007196921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065214,"about_ca_topic_score_gemma":0.0008940942,"domain_scores_codex":[0.9992769,0.0002785575,0.00002178064,0.00006147694,0.0003004454,0.00006079253],"domain_scores_gemma":[0.9984091,0.0009901669,0.0001278653,0.0001118933,0.000327058,0.00003394632],"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":[0.0001514277,0.00004263034,0.001788144,0.0002242502,0.00006071997,0.0004994919,0.000262615,0.6897701,0.03941214,0.2058371,0.001179612,0.06077179],"study_design_scores_gemma":[0.000007255604,0.00006075582,0.000184642,0.00000942299,0.000009085789,0.0001461047,0.000009783859,0.9804695,0.005952652,0.01190462,0.001234238,0.00001193993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08020175,0.001725902,0.9121404,0.0002254728,0.00009161574,0.00003019891,0.00003690036,0.00010939,0.00543842],"genre_scores_gemma":[0.8315486,0.001931938,0.1610902,0.0001796163,0.0001616477,0.0000626682,0.00007821008,0.00003459266,0.00491248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001065214,"threshold_uncertainty_score":0.004398704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207940052280813,"score_gpt":0.2640861891977631,"score_spread":0.2420067886749549,"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."}}