{"id":"W2167353569","doi":"10.1109/icc.2009.5198671","title":"A Simple Near-Capacity Bandwidth-Efficient Coded Modulation Scheme in Rayleigh Fading","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Turbo code; Algorithm; Computer science; Rayleigh fading; Low-density parity-check code; Decoding methods; Fading; Convolutional code; Channel capacity; Bandwidth (computing); Bit error rate; Turbo; Channel (broadcasting); Theoretical computer science; Electronic engineering; Telecommunications; 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":[0.0004762852,0.0006438053,0.0005704775,0.0004137123,0.0003836164,0.000645021,0.0008913254,0.00063179,0.0007010498],"category_scores_gemma":[0.001042115,0.0001795461,0.0002890553,0.0005065877,0.0007646537,0.000865204,0.0007623368,0.0005495619,0.0003986432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004908681,"about_ca_system_score_gemma":0.0005676808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008841041,"about_ca_topic_score_gemma":0.001050909,"domain_scores_codex":[0.9995277,0.0001429165,0.0000197255,0.00005081907,0.0001969182,0.00006185922],"domain_scores_gemma":[0.999625,0.0001074996,0.00004882824,0.00009046217,0.000104186,0.00002404849],"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":[0.000621882,0.0001200857,0.00139514,0.0004274519,0.0001134267,0.0006542245,0.0003454796,0.392507,0.1994404,0.1545622,0.002094602,0.2477181],"study_design_scores_gemma":[0.00004541599,0.0002673198,0.000366402,0.00003536165,0.0000416193,0.0004475444,0.00002935243,0.9486945,0.03631504,0.009316372,0.004382283,0.00005885838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123781,0.00104239,0.8654653,0.0002259175,0.0001228854,0.0001022414,0.00007440908,0.0005965806,0.008589253],"genre_scores_gemma":[0.8469943,0.0004293817,0.1497853,0.00008152613,0.00005063849,0.00005076828,0.00006468398,0.00001983055,0.002523495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008913254,"threshold_uncertainty_score":0.003561556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01951846354488921,"score_gpt":0.2554063669364149,"score_spread":0.2358879033915257,"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."}}