{"id":"W2152747383","doi":"10.1109/isit.2007.4557144","title":"Turbo Equalization for Gray-Coded M-ary QAM with Bit-Level Soft Decisions","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intersymbol interference; Computer science; Equalization (audio); Quadrature amplitude modulation; Bit error rate; Channel (broadcasting); QAM; Algorithm; Symbol (formal); Gray code; Bit (key); Turbo; Electronic engineering; Decoding methods; Telecommunications; Computer network; 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":[0.0004453175,0.000311007,0.0003458295,0.0003128663,0.0002242153,0.0004575819,0.0002643701,0.000476876,0.001522675],"category_scores_gemma":[0.002542568,0.0001455727,0.0002874739,0.000342395,0.0006252563,0.000625184,0.0003868444,0.0003687884,0.0003329704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003814426,"about_ca_system_score_gemma":0.0005111059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007724212,"about_ca_topic_score_gemma":0.001087874,"domain_scores_codex":[0.999684,0.0001047951,0.00001459285,0.00002984255,0.0001244955,0.00004234526],"domain_scores_gemma":[0.9991913,0.0005425836,0.0000643228,0.00007203839,0.0001127768,0.0000169194],"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":[0.0003887986,0.00006304368,0.001530553,0.0002531637,0.00009681639,0.0003262537,0.0002718737,0.6107277,0.0622515,0.1477798,0.001150436,0.17516],"study_design_scores_gemma":[0.000008630021,0.00005373806,0.000273434,0.0000119679,0.00001334831,0.00009420086,0.0000137483,0.9758484,0.0103557,0.01267675,0.0006373521,0.00001273144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06745561,0.0004776927,0.9269714,0.0002027114,0.0000524124,0.00002507069,0.00003086571,0.0001487862,0.004635508],"genre_scores_gemma":[0.8873665,0.0004362079,0.1075915,0.00009992363,0.00003735373,0.00002316335,0.00002761624,0.00002540578,0.004392541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001522675,"threshold_uncertainty_score":0.005093932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04490560833328396,"score_gpt":0.2991663982228764,"score_spread":0.2542607898895925,"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."}}