{"id":"W2117894645","doi":"10.1109/glocom.2003.1258539","title":"Differentially-Encoded Turbo Coded Modulation with APP Channel Estimation","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Turbo code; Computer science; Differential coding; Demodulation; Algorithm; Concatenated error correction code; Decoding methods; Phase-shift keying; Turbo; Low-density parity-check code; Turbo equalizer; Serial concatenated convolutional codes; Channel (broadcasting); Electronic engineering; Bit error rate; Block code; 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":[0.0003612815,0.0004308624,0.0004758362,0.0003075571,0.0002816855,0.0004125855,0.000437589,0.0005801625,0.0005796175],"category_scores_gemma":[0.001478099,0.0001560895,0.000180496,0.000411259,0.0004318925,0.0004243048,0.0003960655,0.0003461062,0.0002443646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003452289,"about_ca_system_score_gemma":0.0005224461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008661526,"about_ca_topic_score_gemma":0.001492743,"domain_scores_codex":[0.9995523,0.0001277885,0.00001780952,0.00003106027,0.0002295107,0.00004160916],"domain_scores_gemma":[0.9992247,0.0002932795,0.00008093324,0.0001353432,0.0002314858,0.00003439574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001359621,0.0001769342,0.007557756,0.0004709976,0.0001889172,0.002079458,0.0003149333,0.3087895,0.4662451,0.04907082,0.00144409,0.1623019],"study_design_scores_gemma":[0.00005102207,0.0003825127,0.0009500562,0.00002329118,0.0000648448,0.001086546,0.00001568867,0.8793758,0.1122081,0.002751203,0.003051335,0.00003975103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4188848,0.0008260426,0.5695832,0.0002874635,0.0001259345,0.0001259336,0.0001051626,0.0005835026,0.009477895],"genre_scores_gemma":[0.9132332,0.0002025713,0.08434145,0.0000410002,0.00003411894,0.0000372268,0.00005031428,0.000012763,0.002047352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008661526,"threshold_uncertainty_score":0.002504826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009163840557503975,"score_gpt":0.2253227120794467,"score_spread":0.2161588715219427,"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."}}