{"id":"W7113900470","doi":"10.1109/jsac.2025.3642218","title":"Covert IRS-UAV Networks Empowered by Deep Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Markov decision process; Covert; Benchmark (surveying); Wireless; Transmission (telecommunications); Markov process; Transmitter power output; Channel (broadcasting); Adversary","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":[0.0005000453,0.0006134722,0.0006034054,0.000201851,0.0002720371,0.00050605,0.0009347216,0.000791038,0.001071317],"category_scores_gemma":[0.001592321,0.0002666799,0.0002800019,0.0002274099,0.0007533805,0.0008514001,0.0009442888,0.0009148795,0.0001910934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007928069,"about_ca_system_score_gemma":0.0007396963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004039524,"about_ca_topic_score_gemma":0.004700754,"domain_scores_codex":[0.9997656,0.00005245202,0.000006650508,0.00005510844,0.0000611117,0.00005911258],"domain_scores_gemma":[0.999478,0.0002489837,0.000094446,0.00004749655,0.00009252284,0.00003848138],"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":[0.00004492886,0.00002735087,0.0004542727,0.0000250849,0.00001377233,0.00005895995,0.00002693954,0.970037,0.002217935,0.005202898,0.0004770679,0.02141368],"study_design_scores_gemma":[0.000001994356,0.00001226405,0.00003047448,0.000001367728,0.000001580788,0.000005905584,0.000002358552,0.9985759,0.0002550599,0.001022485,0.00008949024,0.000001191189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06120501,0.0003605468,0.9337441,0.0002419805,0.00004442334,0.00003202862,0.00004139593,0.000306044,0.004024395],"genre_scores_gemma":[0.9675452,0.0001507548,0.03030168,0.00009535925,0.00001716187,0.00003738181,0.00004555472,0.00001901368,0.001787867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004039524,"threshold_uncertainty_score":0.008032024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380124955118925,"score_gpt":0.2809949964371399,"score_spread":0.2671937468859507,"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."}}