{"id":"W2125511224","doi":"10.1109/ccece.2005.1557352","title":"Triple hybrid selection/maximal-ratio combining in exponentially correlated Rayleigh fading","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Rayleigh fading; Exponential growth; Fading; Maximal-ratio combining; Phase-shift keying; Modulation (music); Selection (genetic algorithm); Mathematics; Keying; Rayleigh scattering; Algorithm; Bit error rate; Fading distribution; Statistics; Computer science; Applied mathematics; Telecommunications; Physics; Mathematical analysis; Optics; Artificial intelligence; Decoding methods; Acoustics","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":[0.002162301,0.000913039,0.0009580859,0.0005514241,0.0003128433,0.001161586,0.0005606301,0.0006236822,0.0009747234],"category_scores_gemma":[0.002507565,0.0003481599,0.0005043535,0.0009250697,0.001049339,0.0008163105,0.0009835163,0.0005117489,0.0006243903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005830381,"about_ca_system_score_gemma":0.0004155502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004954783,"about_ca_topic_score_gemma":0.00053354,"domain_scores_codex":[0.9987279,0.0004556531,0.00005517318,0.0001144549,0.0005012882,0.0001456559],"domain_scores_gemma":[0.9985408,0.0008945445,0.0002005952,0.0001530511,0.0001774735,0.00003362564],"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":[0.0005056848,0.00004438521,0.001980234,0.0002484123,0.0002834433,0.0008887894,0.0002759788,0.7324002,0.05598656,0.117734,0.001347112,0.08830525],"study_design_scores_gemma":[0.0000219135,0.0002096247,0.0006260677,0.00003048241,0.00006968661,0.0005708596,0.00003750562,0.9473296,0.0225842,0.0274111,0.001063578,0.00004534806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0626496,0.0008962739,0.9277534,0.0001117601,0.00004724687,0.00004003417,0.00005506011,0.0004424156,0.008004111],"genre_scores_gemma":[0.937686,0.0008906324,0.05980096,0.00007797084,0.00004789317,0.0000625968,0.00004469157,0.0000357312,0.001353536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002162301,"threshold_uncertainty_score":0.01143551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008037296675945486,"score_gpt":0.2155180246928232,"score_spread":0.2074807280168777,"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."}}