{"id":"W4391188486","doi":"10.1016/j.comcom.2024.01.020","title":"Optimizing Secrecy Energy Efficiency in RIS-assisted MISO systems using Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"Computer Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; Kyungpook National University","keywords":"Reinforcement learning; Secrecy; Maximization; Computer science; Base station; Efficient energy use; Suite; Energy (signal processing); Distributed computing; Mathematical optimization; Artificial intelligence; Computer network; Computer security; Engineering; Mathematics; Electrical engineering","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":[0.0006945699,0.0007927166,0.001069695,0.000293956,0.0004007003,0.001124825,0.0007149039,0.0009327866,0.002642537],"category_scores_gemma":[0.001927172,0.0003680705,0.0002722789,0.0003309759,0.0007372631,0.000853584,0.0009156822,0.001000396,0.000425014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008105743,"about_ca_system_score_gemma":0.001077875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003508431,"about_ca_topic_score_gemma":0.004777124,"domain_scores_codex":[0.999648,0.00008132428,0.00001173027,0.00006676768,0.00007576354,0.0001164435],"domain_scores_gemma":[0.998958,0.0006570349,0.0001009239,0.0000520676,0.0001794369,0.0000526225],"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":[0.00008928072,0.00003542408,0.0003291351,0.00003479983,0.00001593213,0.00004726263,0.00001964365,0.9833665,0.001531791,0.003931758,0.0005007955,0.01009768],"study_design_scores_gemma":[0.000003017817,0.00001327395,0.00003689276,0.000002443858,0.000002621064,0.000005285101,0.000003586313,0.9988797,0.0002428508,0.0007538382,0.00005450739,0.000001863261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1528101,0.001172877,0.8215181,0.0009573576,0.0001637079,0.00007263607,0.0001179397,0.0005269097,0.02266044],"genre_scores_gemma":[0.9904792,0.0000792653,0.006755569,0.0000675754,0.00001836863,0.00001647568,0.00001753295,0.00001893061,0.002547124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003508431,"threshold_uncertainty_score":0.008840203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0348003986732572,"score_gpt":0.2695145420904967,"score_spread":0.2347141434172396,"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."}}