{"id":"W2588149931","doi":"10.1109/access.2017.2667882","title":"Secrecy Energy Efficiency Maximization in Cognitive Radio Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Concordia University","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Beamforming; Computer science; Mathematical optimization; Maximization; Cognitive radio; Utility maximization problem; Quality of service; Transmitter power output; Fractional programming; Convex optimization; Wireless; Mathematics; Regular polygon; Telecommunications; Transmitter","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":[0.001358599,0.001102413,0.0007937979,0.0005180953,0.0004245443,0.001332903,0.0006936167,0.0009404388,0.000620916],"category_scores_gemma":[0.002870468,0.0004396327,0.0005361839,0.0008380865,0.001479825,0.001409176,0.0009613682,0.0006631321,0.0001750894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200485,"about_ca_system_score_gemma":0.000767969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454158,"about_ca_topic_score_gemma":0.0008708801,"domain_scores_codex":[0.9988571,0.0005175806,0.00003194701,0.0001293917,0.0002704367,0.0001935744],"domain_scores_gemma":[0.9985644,0.001036132,0.0001392179,0.00006787299,0.0001553791,0.0000369792],"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":[0.00009309247,0.00003308512,0.0004464732,0.0001158086,0.00004317319,0.00019169,0.0001104362,0.9303781,0.007006919,0.04923922,0.0003876634,0.0119544],"study_design_scores_gemma":[0.000008594995,0.00004875795,0.0001344854,0.000008690441,0.00001329367,0.00005515361,0.00002965382,0.9854519,0.001815597,0.01207093,0.0003529595,0.00001000269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04884299,0.001091635,0.9436421,0.0002765984,0.00003722889,0.00002094217,0.0000329006,0.0000646902,0.005990976],"genre_scores_gemma":[0.9690026,0.001125792,0.02779786,0.00008180092,0.00006597047,0.00004755152,0.00002384155,0.00002208405,0.00183256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001454158,"threshold_uncertainty_score":0.008710146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0322003907322754,"score_gpt":0.3063281055353455,"score_spread":0.2741277148030701,"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."}}