{"id":"W2094148320","doi":"10.1002/ett.1010","title":"Analysis of coherent MPSK schemes with generalized selection diversity in Rayleigh fading","year":2004,"lang":"en","type":"article","venue":"European Transactions on Telecommunications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Medical Research Council; King Fahd University of Petroleum and Minerals","keywords":"Rayleigh fading; Expression (computer science); Phase-shift keying; Fading; Keying; Moment-generating function; Selection (genetic algorithm); Diversity combining; Modulation (music); Algorithm; Computer science; Mathematics; Diversity scheme; Rayleigh scattering; Electronic engineering; Probability density function; Telecommunications; Statistics; Bit error rate; Physics; Engineering; 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":[0.001176976,0.001044158,0.0006948274,0.0006037364,0.0003569924,0.0009517342,0.0006216739,0.0007183292,0.001806377],"category_scores_gemma":[0.003315308,0.0003709476,0.0004950088,0.0009599567,0.001035464,0.001111111,0.0009550679,0.0005138463,0.0003511046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143361,"about_ca_system_score_gemma":0.0006795867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439954,"about_ca_topic_score_gemma":0.00125305,"domain_scores_codex":[0.998919,0.0003428834,0.00003384864,0.00006948033,0.0005087564,0.0001260953],"domain_scores_gemma":[0.9979135,0.001250255,0.0002609833,0.0001762831,0.0003519133,0.00004707367],"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":[0.0001714347,0.00003383972,0.0008944257,0.0001806025,0.00008534852,0.0003454565,0.0001664944,0.8748425,0.02540471,0.07810809,0.0007826499,0.01898442],"study_design_scores_gemma":[0.000009116266,0.0000474409,0.0005513608,0.00001136501,0.00001545175,0.0001085724,0.00001525766,0.9878185,0.001735913,0.009300134,0.0003754261,0.00001151382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2758926,0.003583824,0.6960344,0.0006796886,0.00005829243,0.0001199806,0.0002184292,0.0003212153,0.02309159],"genre_scores_gemma":[0.972706,0.001210675,0.02253522,0.00009038652,0.0001001034,0.00006631139,0.00009858476,0.00006267732,0.003130029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001806377,"threshold_uncertainty_score":0.01040161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162674496828469,"score_gpt":0.2439785724252331,"score_spread":0.2223518274569484,"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."}}