{"id":"W2765717969","doi":"10.1109/tvt.2017.2764387","title":"Multiantenna Spectrum Sensing Over Correlated Nakagami-$m$ Channels With MRC and EGC Diversity Receptions","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nakagami distribution; Maximal-ratio combining; Spectrum (functional analysis); Diversity (politics); Diversity combining; Statistics; Physics; Electronic engineering; Telecommunications; Computer science; Mathematics; Fading; Engineering; Political science; Channel (broadcasting)","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":[0.001066418,0.000497811,0.0007442842,0.0003557804,0.0003361949,0.0008233045,0.0005526445,0.0005802062,0.0003134126],"category_scores_gemma":[0.002226269,0.0003359029,0.0003214957,0.0005343039,0.001025483,0.0007854253,0.0008459589,0.0003311195,0.0001293457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005493012,"about_ca_system_score_gemma":0.0005282384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276355,"about_ca_topic_score_gemma":0.00193165,"domain_scores_codex":[0.9989793,0.0004572244,0.00004176479,0.0001533993,0.0002441825,0.0001241415],"domain_scores_gemma":[0.9982318,0.001062563,0.0002800205,0.0001998184,0.0001663095,0.00005950569],"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":[0.0009738832,0.00007928272,0.007245505,0.0001513802,0.0001731393,0.001592205,0.0002927804,0.8584715,0.06548295,0.02133524,0.0003095707,0.04389253],"study_design_scores_gemma":[0.0000142244,0.00009797882,0.001044077,0.000009546235,0.00002659007,0.0002779941,0.00004611191,0.984542,0.01093105,0.002853315,0.0001407597,0.00001623559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5446693,0.0004211325,0.4493077,0.0001950706,0.00001965417,0.00003710625,0.00005267187,0.0002272545,0.005070063],"genre_scores_gemma":[0.9858516,0.00005885126,0.01381233,0.00002229583,0.000005708563,0.000006486601,0.000009283991,0.000002834343,0.0002305678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001276355,"threshold_uncertainty_score":0.005639791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168138777587023,"score_gpt":0.2182083862193991,"score_spread":0.2065269984435288,"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."}}