{"id":"W3004619250","doi":"10.1109/jsyst.2020.2967470","title":"Artificial-Noise-Aided Energy-Efficient Secure Beamforming for Multi-Eavesdroppers in Cognitive Radio Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Graduate Research and Innovation Projects of Jiangsu Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial noise; Eavesdropping; Computer science; Beamforming; Cognitive radio; Base station; Channel state information; Transmitter power output; Secure transmission; Maximization; Efficient energy use; Secrecy; Convex optimization; Computer network; Mathematical optimization; Wireless; Channel (broadcasting); Transmitter; Telecommunications; Mathematics; Engineering; Electrical engineering; Regular polygon; Encryption; Computer security","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":[0.0009969558,0.001055613,0.0007452122,0.0003501123,0.0003908951,0.0008046849,0.0007058501,0.0008318314,0.0005266392],"category_scores_gemma":[0.001991901,0.0003828601,0.0006017628,0.0006259161,0.00109197,0.001061125,0.0008303089,0.0007668443,0.0001620515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007211561,"about_ca_system_score_gemma":0.000874766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002527864,"about_ca_topic_score_gemma":0.001963832,"domain_scores_codex":[0.9993017,0.0002827529,0.00002200951,0.00009030708,0.0001912115,0.000112013],"domain_scores_gemma":[0.9991573,0.0005789387,0.00007842975,0.00003790709,0.0001222625,0.00002528405],"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":[0.00006494418,0.00002695904,0.0003245193,0.00005911284,0.00003433079,0.00007537437,0.00006068688,0.9559233,0.005653417,0.0219025,0.0002774986,0.01559742],"study_design_scores_gemma":[0.000002963256,0.00001618794,0.00002904518,0.000001928954,0.000004349963,0.00001120548,0.000006694001,0.9974706,0.0005430325,0.001823508,0.0000868961,0.000003592012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0155221,0.0003662265,0.9824767,0.0001040562,0.00002318024,0.000009921648,0.00001006962,0.00004777516,0.001440042],"genre_scores_gemma":[0.8922803,0.001068054,0.1040205,0.0001300047,0.0000515406,0.00007390084,0.00004099033,0.00002682092,0.002307879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002527864,"threshold_uncertainty_score":0.005272508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05853802843976318,"score_gpt":0.2823687537131773,"score_spread":0.2238307252734141,"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."}}