{"id":"W4231630489","doi":"10.32920/14639013.v1","title":"Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Multipath propagation; Fading; Detector; Rayleigh fading; Computer science; Eigenvalues and eigenvectors; Electronic engineering; Spectrum (functional analysis); Algorithm; Telecommunications; Wireless; Physics; Engineering; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001464384,0.0009353096,0.0009302527,0.0003393869,0.0005118608,0.000997216,0.0007378472,0.0008640655,0.0004898006],"category_scores_gemma":[0.005867202,0.0003281638,0.0005203191,0.0005268305,0.001697924,0.001104458,0.001573137,0.0006738674,0.0001259867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005064015,"about_ca_system_score_gemma":0.0006570379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002305262,"about_ca_topic_score_gemma":0.001750227,"domain_scores_codex":[0.998415,0.0005792984,0.00003351195,0.0002097228,0.0004689201,0.000293528],"domain_scores_gemma":[0.9962828,0.002756769,0.0002666241,0.0002110084,0.0003431968,0.0001396453],"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":[0.0008259291,0.0001434635,0.004034018,0.0002283004,0.0001949285,0.001515367,0.0005959603,0.8755653,0.03178108,0.04409472,0.0006172902,0.04040375],"study_design_scores_gemma":[0.00002043588,0.0001570718,0.0006023871,0.000006219677,0.00002347759,0.0002492788,0.0001102684,0.9813763,0.005206501,0.01206194,0.0001670137,0.00001904002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2576757,0.0004253724,0.7373834,0.0002429185,0.00005151958,0.00004191422,0.00002848255,0.0001489585,0.004001753],"genre_scores_gemma":[0.9821467,0.0001820923,0.01695533,0.00003828224,0.0000216416,0.0000161939,0.000007795004,0.000007382421,0.0006244858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002305262,"threshold_uncertainty_score":0.007744491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712999392677825,"score_gpt":0.2262354879552649,"score_spread":0.2091054940284866,"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."}}