{"id":"W2110487308","doi":"10.1109/tcomm.2005.861670","title":"Approximate SER of H-S/MRC in Nakagami fading with arbitrary branch power correlation","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nakagami distribution; Fading; Mathematics; Maximal-ratio combining; Integer (computer science); Modulation (music); Phase-shift keying; Statistics; Keying; Power (physics); Bit error rate; Algorithm; Telecommunications; Computer science; Physics; Decoding methods","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001264637,0.0001849894,0.0002210472,0.000371517,0.0001502164,0.00001674301,0.0006210252,0.0001108377,0.00003082086],"category_scores_gemma":[0.000002147318,0.0002005087,0.00006173226,0.0007133414,0.0001596356,0.0003592503,0.000005103932,0.0005442617,0.00001083451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001285851,"about_ca_system_score_gemma":0.00002478399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001026789,"about_ca_topic_score_gemma":0.0004814827,"domain_scores_codex":[0.9989578,0.00008322473,0.000464601,0.0001496258,0.0001560438,0.0001887345],"domain_scores_gemma":[0.9977487,0.0002462277,0.00009083698,0.00180025,0.00008082419,0.00003320767],"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":[0.00003238547,0.0005105671,0.0006702903,0.00005411243,0.00004309173,6.772461e-7,0.0005483261,0.9737512,0.01192173,0.005878837,0.00007130551,0.00651745],"study_design_scores_gemma":[0.002645328,0.0002615242,0.01265601,0.001096751,0.00009906448,0.00004247889,0.000618034,0.7113167,0.2526554,0.01338096,0.003661288,0.001566459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03197063,0.000320234,0.9523396,0.0001605486,0.00005410709,0.0003909407,0.0000365928,0.0006247266,0.01410265],"genre_scores_gemma":[0.9607244,0.000441772,0.038409,0.00001701286,0.000003743689,0.0002308635,0.00002933296,0.00005625653,0.00008767714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9287537,"threshold_uncertainty_score":0.8176509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054698259198894,"score_gpt":0.2291825001758941,"score_spread":0.2186355175839051,"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."}}