{"id":"W2583715925","doi":"10.1109/glocom.2016.7841702","title":"Correlated Multichannel Spectrum Sensing Cognitive Radio System with Selection Combining","year":2016,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Fading; Nakagami distribution; Computer science; Detector; Channel (broadcasting); Selection (genetic algorithm); Spectrum (functional analysis); Correlation; Shadow mapping; Algorithm; Electronic engineering; Telecommunications; Mathematics; Artificial intelligence; Wireless; Engineering; Physics","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":[0.001219056,0.0008340569,0.0008200594,0.0003246679,0.000438441,0.0009192155,0.0007838025,0.000531324,0.0003963868],"category_scores_gemma":[0.001709729,0.0003378179,0.0004574698,0.0007121467,0.0007594357,0.0007752514,0.0007442346,0.0005015229,0.0001156728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005451859,"about_ca_system_score_gemma":0.0008465218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425646,"about_ca_topic_score_gemma":0.001577224,"domain_scores_codex":[0.9989204,0.000364167,0.00004369348,0.0002065226,0.0003214088,0.0001437474],"domain_scores_gemma":[0.9983803,0.0009430827,0.0002139896,0.0001721253,0.0002224126,0.00006807114],"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":[0.0006454483,0.0002268054,0.004314739,0.0001757697,0.000334942,0.001324435,0.0002527176,0.8727426,0.03349727,0.02098343,0.0007895124,0.06471236],"study_design_scores_gemma":[0.00003124042,0.000199173,0.0006185235,0.000006067718,0.00005611949,0.0002763229,0.00002064403,0.990733,0.003614626,0.004222534,0.000200095,0.0000216192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3216471,0.000660587,0.672259,0.0002092799,0.00006170783,0.00007543784,0.00005814638,0.0002871445,0.004741532],"genre_scores_gemma":[0.9845707,0.0001260634,0.01482089,0.00004918138,0.00001802436,0.00002279604,0.00001210362,0.000003959016,0.0003763318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001425646,"threshold_uncertainty_score":0.006447017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00922181854077975,"score_gpt":0.2020014342408246,"score_spread":0.1927796157000449,"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."}}