{"id":"W4379928101","doi":"10.1109/twc.2023.3281812","title":"Joint Power Allocation and 3D Deployment for UAV-BSs: A Game Theory Based Deep Reinforcement Learning Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Algoma University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Markov decision process; Base station; Software deployment; Throughput; Telecommunications link; Wireless; Game theory; Power control; Flexibility (engineering); Real-time computing; Computer network; Distributed computing; Markov process; Power (physics); Artificial intelligence; 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":[0.0003419933,0.0001932564,0.0001717986,0.0002985751,0.0005289313,0.00006817262,0.0002790874,0.0001068593,0.00002212023],"category_scores_gemma":[0.000006883092,0.0002138424,0.00008277337,0.0005373806,0.00008960378,0.0001446921,0.000005876384,0.0002766975,0.00003321885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284319,"about_ca_system_score_gemma":0.00002852738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001058868,"about_ca_topic_score_gemma":0.0000225171,"domain_scores_codex":[0.9989491,0.00008186172,0.0003542313,0.0002232202,0.0001489366,0.0002426541],"domain_scores_gemma":[0.9985762,0.000261309,0.000062924,0.0009094764,0.0001049259,0.00008513386],"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":[0.00001307642,0.0001000263,0.000002747393,0.00004601319,0.00005772128,4.72342e-8,0.0006705702,0.9735018,0.001574331,0.003788801,0.00004548101,0.02019941],"study_design_scores_gemma":[0.0005170981,0.00005620607,0.00006487443,0.00003357659,0.00006042903,0.000001262581,0.0003251024,0.9938901,0.002446805,0.0001283513,0.002253988,0.0002221926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003587714,0.00009482232,0.9930664,0.000358832,0.0000740916,0.0009099005,0.00001607512,0.0008464823,0.001045659],"genre_scores_gemma":[0.975987,0.000940286,0.01978353,0.00007162838,0.00001004159,0.002619262,0.0002339628,0.00007150097,0.0002827744],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9732829,"threshold_uncertainty_score":0.8720238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351581189547172,"score_gpt":0.2385382603697375,"score_spread":0.2150224484742658,"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."}}