{"id":"W4318823904","doi":"10.1109/lwc.2023.3238510","title":"Optimal Hovering Height and Power Allocation for UAV-Aided NOMA Covert Communication System","year":2023,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Covert; Computer science; Telecommunications link; Transmission (telecommunications); Transmitter power output; Signal-to-noise ratio (imaging); Power (physics); Real-time computing; Geometric programming; Mathematical optimization; Power budget; Power control; Computer network; Transmitter; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002965373,0.0002048051,0.0002137798,0.0002192735,0.0005140877,0.0001321229,0.0007324297,0.0001069039,0.000003203456],"category_scores_gemma":[0.00001206728,0.0002419574,0.00006485574,0.0005502101,0.0001272947,0.0003400561,0.0001303395,0.0001792594,0.00004912469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001843784,"about_ca_system_score_gemma":0.00001578046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003510449,"about_ca_topic_score_gemma":0.00002918244,"domain_scores_codex":[0.9988981,0.00006712152,0.0004067027,0.0002223185,0.0001349686,0.0002707414],"domain_scores_gemma":[0.997669,0.0003045866,0.00009792336,0.001745064,0.0001086592,0.00007477351],"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":[0.00004314748,0.0001285474,0.0008289012,0.0006023832,0.000355555,0.000001372268,0.003692419,0.7474633,0.1644667,0.04291786,0.02874628,0.01075347],"study_design_scores_gemma":[0.0005378106,0.00001451822,0.001580458,0.0001147468,0.00004260069,0.000007907673,0.0005376888,0.9849768,0.002460413,0.00002394233,0.009356913,0.0003461983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6908739,0.0005413417,0.3001221,0.004083205,0.000237406,0.001222307,0.00008635828,0.0017603,0.001073101],"genre_scores_gemma":[0.9762077,0.0009783159,0.02103207,0.0001611596,0.00003196735,0.0009699287,0.0005043696,0.00007745164,0.00003708054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2853337,"threshold_uncertainty_score":0.9866737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322947580708895,"score_gpt":0.2227037479740913,"score_spread":0.2094742721670024,"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."}}