{"id":"W4239273961","doi":"10.32920/14637045","title":"Throughput Maximization Based on Optimal Access Probabilities in Cognitive Radio System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Underlay; Cognitive radio; Overlay; Computer science; Computer network; Throughput; Scheme (mathematics); Markov process; Maximization; Telecommunications; Signal-to-noise ratio (imaging); Mathematical optimization; Wireless; Mathematics","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":[0.001545483,0.0008492359,0.001330076,0.0007399812,0.0005958226,0.002139525,0.0009198674,0.0009740953,0.001493024],"category_scores_gemma":[0.005933248,0.000670162,0.0006113039,0.001084045,0.001705635,0.001964645,0.00110789,0.001108655,0.000239489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002210426,"about_ca_system_score_gemma":0.002109828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003762181,"about_ca_topic_score_gemma":0.002330265,"domain_scores_codex":[0.9982153,0.0007406974,0.00005106389,0.0002617658,0.0004052611,0.0003258912],"domain_scores_gemma":[0.9971843,0.002173365,0.0002370035,0.0001014049,0.0002222189,0.00008171429],"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":[0.0001038344,0.00006120399,0.0004753265,0.0001389292,0.00005419727,0.000237049,0.0001727134,0.8711646,0.004173854,0.1116367,0.001152824,0.01062888],"study_design_scores_gemma":[0.000007362282,0.00001708124,0.0001044244,0.000008448686,0.000008440487,0.00003238567,0.00001427575,0.9744214,0.0004549348,0.02475724,0.0001650908,0.000008832641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06935503,0.001147873,0.9173201,0.0006335423,0.00005853517,0.00005984333,0.0001207736,0.0002726034,0.01103168],"genre_scores_gemma":[0.9783749,0.0008002456,0.01866512,0.00007235655,0.00007712687,0.0000770214,0.00004163687,0.00003318198,0.001858386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003762181,"threshold_uncertainty_score":0.01603782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493102983520968,"score_gpt":0.2790249010132846,"score_spread":0.2440938711780749,"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."}}