{"id":"W2065708403","doi":"10.3390/electronics3030553","title":"Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels","year":2014,"lang":"en","type":"article","venue":"Electronics","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Multipath propagation; Fading; Detector; Rayleigh fading; Computer science; Electronic engineering; Eigenvalues and eigenvectors; Rayleigh scattering; Algorithm; Wireless; Telecommunications; Engineering; Physics; Channel (broadcasting); Optics","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":[0.001224568,0.0008494646,0.0008889735,0.0003037103,0.0004958058,0.0009483276,0.000645207,0.0007972699,0.0004530885],"category_scores_gemma":[0.004226505,0.0002949467,0.0004953015,0.0003875986,0.001606863,0.001069349,0.001243926,0.0005367852,0.0001116782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004330581,"about_ca_system_score_gemma":0.0005193526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001811494,"about_ca_topic_score_gemma":0.001364053,"domain_scores_codex":[0.9988632,0.0004108596,0.00002624099,0.0001527906,0.0003313601,0.0002155506],"domain_scores_gemma":[0.9972979,0.001978864,0.0001963314,0.000157861,0.0002638749,0.000105106],"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":[0.0008873052,0.0001682147,0.004411173,0.0002182756,0.0002016152,0.001878142,0.0006471234,0.8665604,0.04523038,0.04101502,0.0004475341,0.03833489],"study_design_scores_gemma":[0.00001939512,0.0001960579,0.0006798935,0.000006323934,0.00002557769,0.000273821,0.0001059046,0.9824813,0.006599967,0.009442974,0.0001480188,0.00002070183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3601587,0.0003978397,0.6343934,0.0002185921,0.00004641905,0.0000430451,0.00002223725,0.0001418974,0.004577858],"genre_scores_gemma":[0.9868066,0.0001363207,0.01250474,0.00003030505,0.000014599,0.00001128719,0.000004711962,0.000004865776,0.0004865117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001811494,"threshold_uncertainty_score":0.006476164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008314721814776209,"score_gpt":0.2088082910808383,"score_spread":0.2004935692660621,"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."}}