{"id":"W4315629807","doi":"10.1109/globecom48099.2022.10001383","title":"Resource Management for Heterogeneous Aerial Networks with Backhaul Constraints","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; McGill University","funders":"National Key Research and Development Program of China; Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Backhaul (telecommunications); Computer science; Base station; Computer network; Power control; Telecommunications link; Wireless network; Cellular network; Maximization; Wireless; Distributed computing; Mathematical optimization; Power (physics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000546769,0.0008819052,0.0007052201,0.0004261217,0.0005368162,0.001232787,0.0009779698,0.0006622255,0.001891075],"category_scores_gemma":[0.001079114,0.0002836223,0.0003087764,0.0008403238,0.0005653948,0.001427191,0.00109237,0.0005490201,0.000168278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098599,"about_ca_system_score_gemma":0.000545438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006000168,"about_ca_topic_score_gemma":0.005020328,"domain_scores_codex":[0.9996197,0.0001037875,0.00001135198,0.00009260225,0.00007203059,0.0001005588],"domain_scores_gemma":[0.9995164,0.0002894384,0.00008096638,0.00002504688,0.00004832423,0.0000397458],"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":[0.00004478498,0.00003075753,0.0003490835,0.00006177834,0.00003285469,0.0003051184,0.0000450017,0.9667702,0.002323937,0.01297144,0.001092158,0.01597295],"study_design_scores_gemma":[0.00001142786,0.00002539074,0.0001848464,0.000004346869,0.000009119578,0.00002885719,0.00004340041,0.9943212,0.0003884678,0.00437967,0.0005985068,0.00000464738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1008691,0.001309386,0.8863197,0.0004783306,0.00006917108,0.000093116,0.0002178858,0.000157385,0.01048601],"genre_scores_gemma":[0.9637372,0.0005007607,0.03264107,0.00009393135,0.00005377285,0.00008582517,0.0001210932,0.00003034182,0.002735967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006000168,"threshold_uncertainty_score":0.01193047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728626034024365,"score_gpt":0.2350462819268435,"score_spread":0.2177600215865999,"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."}}