{"id":"W2786549485","doi":"10.1109/pimrc.2017.8292608","title":"Low-cost code-aided ML timing recovery from turbo-coded QAM transmissions","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; University of Toronto","funders":"","keywords":"Turbo code; Computer science; Turbo; Turbo equalizer; Algorithm; Synchronization (alternating current); Estimator; QAM; Code (set theory); Maximum likelihood; Quadrature amplitude modulation; Decoding methods; Bit error rate; Statistics; Concatenated error correction code; Mathematics; Block code; Telecommunications; Channel (broadcasting)","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":[0.0007621653,0.0006268976,0.0008070851,0.0004399203,0.0003392682,0.0006181234,0.0007985272,0.0007636582,0.0009461363],"category_scores_gemma":[0.003924426,0.0003729524,0.0003778153,0.0006076716,0.0005542564,0.001329248,0.001097623,0.0008622552,0.0004625679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003280259,"about_ca_system_score_gemma":0.001111179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009083694,"about_ca_topic_score_gemma":0.0013647,"domain_scores_codex":[0.9994475,0.0001533747,0.00002337495,0.00006039865,0.0002761347,0.00003915393],"domain_scores_gemma":[0.998908,0.0006351516,0.0001298952,0.0001490181,0.0001535951,0.00002431625],"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":[0.0005597347,0.00005506867,0.0013229,0.0004006685,0.0000965471,0.0004082694,0.0002848536,0.4771591,0.08575948,0.05021757,0.001783206,0.3819527],"study_design_scores_gemma":[0.00002611939,0.00005987722,0.0001738331,0.00001881477,0.0000184347,0.0001991988,0.00001427835,0.9759773,0.01661737,0.005418642,0.00145333,0.00002278143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01001679,0.0002568348,0.9886412,0.0001070985,0.00002836472,0.00001694268,0.00002693703,0.0001920336,0.0007137289],"genre_scores_gemma":[0.482377,0.0005662939,0.5137315,0.0001425211,0.0001084903,0.0000932066,0.0001284359,0.00005838725,0.002794192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009461363,"threshold_uncertainty_score":0.004030764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03432705336438031,"score_gpt":0.2881601253411167,"score_spread":0.2538330719767364,"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."}}