{"id":"W2161900079","doi":"10.1109/vetecf.2005.1558058","title":"Low-complexity BCJR decoder for turbo decoders and its VLSI implementation in 0.18-&amp;#x03BC;m CMOS","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbo equalizer; Turbo code; BCJR algorithm; Soft-decision decoder; Computer science; Serial concatenated convolutional codes; Turbo; Very-large-scale integration; Decoding methods; CMOS; Difference-map algorithm; Algorithm; Concatenated error correction code; Electronic engineering; Block code; Embedded system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003402892,0.0003815501,0.000250852,0.0003942148,0.0002963908,0.0005135408,0.0006904848,0.0005434749,0.003428504],"category_scores_gemma":[0.001417303,0.0002215182,0.0002246373,0.0003734303,0.0003085963,0.0005604401,0.0002026588,0.0004611153,0.001456164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004211776,"about_ca_system_score_gemma":0.0007435445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009914549,"about_ca_topic_score_gemma":0.002434279,"domain_scores_codex":[0.9996375,0.00006618171,0.00002689625,0.00003785928,0.0002078206,0.00002372332],"domain_scores_gemma":[0.9995621,0.0001526185,0.00004316596,0.00004535867,0.0001795225,0.00001725948],"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":[0.0003303406,0.00008631918,0.001025004,0.0007654635,0.00008913733,0.0004882718,0.0002838598,0.02978436,0.3267855,0.05793187,0.008402981,0.5740269],"study_design_scores_gemma":[0.0001829433,0.001036291,0.001757176,0.0001116166,0.00012688,0.003594429,0.0000653511,0.5139839,0.3844086,0.01130393,0.08333592,0.0000929733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01207455,0.0007844684,0.9801093,0.0002163937,0.00008938619,0.00006480177,0.00006936368,0.001003128,0.005588577],"genre_scores_gemma":[0.1727193,0.0008791318,0.8169391,0.0001455966,0.0001228384,0.0001121148,0.0001361996,0.00008068087,0.008864939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003428504,"threshold_uncertainty_score":0.01146954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03808409113232897,"score_gpt":0.3330842360317031,"score_spread":0.2950001448993741,"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."}}