{"id":"W2105568424","doi":"10.1109/lcomm.2004.825722","title":"Performance Analysis of&amp;lt;tex&amp;gt;$L$&amp;lt;/tex&amp;gt;-Branch Equal Gain Combiners in Equally Correlated Rayleigh Fading Channels","year":2004,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fading; Rayleigh fading; Fading distribution; Mathematics; Cumulative distribution function; Signal-to-noise ratio (imaging); Statistics; Telecommunications; Probability density function; Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001020422,0.0006889334,0.001122894,0.002055518,0.000446737,0.00009988429,0.003757285,0.0003760372,0.00008086803],"category_scores_gemma":[0.0001971148,0.0008374823,0.0003923465,0.004651623,0.0005974043,0.0007695854,0.0005236299,0.001412875,0.000169047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008757315,"about_ca_system_score_gemma":0.00007189266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001751053,"about_ca_topic_score_gemma":0.001424668,"domain_scores_codex":[0.9957579,0.0004650957,0.001772123,0.0005535216,0.0005776016,0.0008737547],"domain_scores_gemma":[0.9916596,0.0008825267,0.0005572998,0.006419652,0.0002756612,0.0002052814],"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":[0.00002687292,0.0002301906,0.001189573,0.00007552354,0.0005694418,8.298388e-7,0.003549257,0.7936515,0.1960529,0.0009525035,0.0008157605,0.00288566],"study_design_scores_gemma":[0.01989282,0.0004758987,0.02756415,0.008052059,0.005969747,0.0001374347,0.000894537,0.6228341,0.1015888,0.004649291,0.1913159,0.01662529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6597962,0.001507931,0.3338951,0.00155554,0.0002563318,0.0006472399,0.00006485857,0.001077556,0.001199301],"genre_scores_gemma":[0.940508,0.003280862,0.05398855,0.0005204237,0.00003376345,0.0003509215,0.001040682,0.0001644405,0.0001123553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2807119,"threshold_uncertainty_score":0.9994076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04192686751588409,"score_gpt":0.2873090897921587,"score_spread":0.2453822222762747,"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."}}