{"id":"W2154525254","doi":"10.1109/glocom.2007.63","title":"Asymptotic Gains of Generalized Selection Combining Over Correlated Fading Channels","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Fading; Maximal-ratio combining; Rician fading; Mathematics; Signal-to-noise ratio (imaging); Fading distribution; Diversity gain; Covariance matrix; Moment-generating function; Channel (broadcasting); Covariance; Diversity scheme; Algorithm; Statistics; Telecommunications; Topology (electrical circuits); Computer science; Probability density function; Rayleigh fading; Combinatorics","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":[0.001504467,0.001069768,0.0007210267,0.0007308936,0.0003435999,0.001017219,0.0007061925,0.0005570881,0.001707257],"category_scores_gemma":[0.008114417,0.0003958096,0.000558308,0.001088215,0.001264568,0.001600739,0.001047475,0.0006819617,0.0004463277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009998374,"about_ca_system_score_gemma":0.0007459554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007714693,"about_ca_topic_score_gemma":0.0007438262,"domain_scores_codex":[0.9990242,0.0003084858,0.00003242039,0.0001028686,0.0003791041,0.0001530497],"domain_scores_gemma":[0.9957685,0.002933383,0.0003974566,0.000430693,0.0004161252,0.0000538212],"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":[0.0001615535,0.00003296178,0.001956651,0.000135377,0.0001210251,0.000440656,0.0002199612,0.7714404,0.01192321,0.1626438,0.001194918,0.04972943],"study_design_scores_gemma":[0.00002338791,0.00008414386,0.001445461,0.00003738529,0.00006909666,0.0006008723,0.00005146123,0.898119,0.006293769,0.09189596,0.001336082,0.00004319434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1733104,0.001246099,0.7991101,0.0004349801,0.00003239528,0.00004440168,0.0001818298,0.0008840648,0.02475568],"genre_scores_gemma":[0.9614521,0.0008271805,0.03624586,0.0001435094,0.00006344043,0.00006551331,0.0001293236,0.00005189009,0.00102112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001707257,"threshold_uncertainty_score":0.007956445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889275877012532,"score_gpt":0.2799323690393369,"score_spread":0.2610396102692116,"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."}}