{"id":"W4381304734","doi":"10.1109/tvt.2023.3281470","title":"Secure Transmission for STAR-RIS Aided NOMA Against Internal Eavesdropping","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Eavesdropping; Telecommunications link; Maximization; Beamforming; Computer science; Mathematical optimization; Secrecy; Convex optimization; Transmission (telecommunications); Wireless; Optimization problem; Secure transmission; Transmitter power output; Artificial noise; Regular polygon; Transmitter; Computer network; Algorithm; Mathematics; Telecommunications; Physical layer; Channel (broadcasting)","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":[0.000576681,0.0009314943,0.0006332304,0.0002860402,0.000483424,0.0007188558,0.0005201163,0.0005368001,0.001151564],"category_scores_gemma":[0.001039278,0.0003044849,0.0005666702,0.0004593427,0.0008104618,0.0007874352,0.001341734,0.000688482,0.0004954434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003123706,"about_ca_system_score_gemma":0.0006717082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005791136,"about_ca_topic_score_gemma":0.0008715027,"domain_scores_codex":[0.9993339,0.0002693488,0.00002806254,0.0001005515,0.000147911,0.0001200924],"domain_scores_gemma":[0.9994065,0.0002640813,0.0001184375,0.00009013621,0.0000887073,0.00003211902],"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":[0.00070049,0.00008868186,0.001689377,0.0002008078,0.0001224974,0.0007630261,0.0003094005,0.7569104,0.09691446,0.05450936,0.001987792,0.08580373],"study_design_scores_gemma":[0.00001601299,0.0001439981,0.0001523809,0.000007709317,0.00002127986,0.0001376895,0.00003375172,0.9863384,0.00850542,0.003904224,0.0007250444,0.00001413822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06008384,0.0002166157,0.9348176,0.0001871955,0.00005005798,0.00002397012,0.00003770443,0.0002134444,0.004369617],"genre_scores_gemma":[0.953733,0.0002115951,0.04404126,0.00009047068,0.00003700346,0.00005216902,0.0000424212,0.00002042968,0.001771755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001151564,"threshold_uncertainty_score":0.003852367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329187268833014,"score_gpt":0.2441679421044361,"score_spread":0.230876069416106,"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."}}