{"id":"W4401747727","doi":"10.1109/taes.2024.3447636","title":"Causal CSI-Based Trajectory Design and Power Allocation for UAV-Enabled Wireless Networks Under Average Rate Constraints: A Constrained Reinforcement Learning Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Wireless; Reinforcement learning; Computer science; Wireless network; Power (physics); Mathematical optimization; Control theory (sociology); Artificial intelligence; Control (management); Telecommunications; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004202392,0.0002457426,0.0002265343,0.0001339668,0.0002453798,0.0002103014,0.00004893859,0.0001657867,0.0000139484],"category_scores_gemma":[0.000001416461,0.0002470701,0.00005340379,0.0002423826,0.00008378164,0.0001186226,4.945879e-7,0.0003252309,0.000002157679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002222735,"about_ca_system_score_gemma":0.0001229804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002206706,"about_ca_topic_score_gemma":0.00001468432,"domain_scores_codex":[0.9988267,0.0000728646,0.0002665782,0.0003249313,0.00010189,0.0004069844],"domain_scores_gemma":[0.999495,0.0001944747,0.00003748278,0.0001348568,0.00005064762,0.00008755457],"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":[0.00004184256,0.00002173163,6.739077e-7,0.0001828128,0.0001453778,4.561991e-7,0.0001894425,0.9927005,0.003661391,0.001961587,0.00007879305,0.001015423],"study_design_scores_gemma":[0.0008215645,0.0002141822,0.000002811122,0.0001072456,0.00008298366,0.00001823578,0.0003146793,0.9967651,0.001143945,0.00001136267,0.000259623,0.0002582902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005916846,0.0009112029,0.9910793,0.00006167356,0.0002251063,0.001281228,0.000006235095,0.0003447827,0.0001735891],"genre_scores_gemma":[0.9974755,0.0004918339,0.0007102866,0.00003128179,0.00003701404,0.0007603203,0.00002964939,0.00005967751,0.0004044463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9915587,"threshold_uncertainty_score":0.9999982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008701175931554683,"score_gpt":0.203100184435692,"score_spread":0.1943990085041374,"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."}}