{"id":"W2159230224","doi":"10.1109/cjece.2007.364328","title":"Iterative channel estimation and decoding of turbo-coded OFDM symbols in selective Rayleigh channel","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Decoding methods; Channel (broadcasting); Computer science; Turbo; Turbo code; Orthogonal frequency-division multiplexing; Algorithm; Electronic engineering; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007502976,0.0006112444,0.0007472417,0.0004904729,0.0003987787,0.0005237824,0.0005341534,0.0007980943,0.0004455279],"category_scores_gemma":[0.006553378,0.00036102,0.0004281062,0.0005735715,0.0007858486,0.00094262,0.0007406609,0.0005240004,0.0003146371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004147197,"about_ca_system_score_gemma":0.001335658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003414992,"about_ca_topic_score_gemma":0.003179,"domain_scores_codex":[0.9991161,0.0003461908,0.00004522275,0.00009894866,0.0002831107,0.0001103468],"domain_scores_gemma":[0.9971249,0.001936047,0.0002331457,0.0001926431,0.000463938,0.00004936398],"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":[0.0003011585,0.00003912935,0.001853639,0.0001267624,0.00007276784,0.0005337281,0.0003519995,0.8548871,0.02404822,0.01328597,0.0005779544,0.1039216],"study_design_scores_gemma":[0.0000089649,0.00004421562,0.0002820576,0.000007375865,0.00001012678,0.0001312517,0.0000186101,0.9845675,0.0124644,0.00222909,0.0002210819,0.00001525794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0776453,0.0003263848,0.9201499,0.00008393637,0.00002634966,0.00003021879,0.0000412111,0.0002735739,0.001423167],"genre_scores_gemma":[0.7899756,0.0004661235,0.2073906,0.00004893417,0.00002852729,0.00005062153,0.00008645646,0.00004700462,0.001906177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003414992,"threshold_uncertainty_score":0.006790221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006088765160597822,"score_gpt":0.2025500916148738,"score_spread":0.196461326454276,"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."}}