{"id":"W2953406779","doi":"10.1109/tmc.2019.2926713","title":"Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Toronto","funders":"","keywords":"Computer science; Base station; Orthogonal frequency-division multiple access; Computational complexity theory; Mathematical optimization; Wireless network; Integer programming; Profit maximization; Optimization problem; Heterogeneous network; Resource allocation; Linear programming; Cellular network; Wireless; Computer network; Orthogonal frequency-division multiplexing; Profit (economics); Channel (broadcasting); Algorithm; 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":[0.00006575762,0.0001336425,0.0001245188,0.0001327845,0.0001026533,0.00005963289,0.00005115962,0.00006629166,0.00004220724],"category_scores_gemma":[4.425872e-7,0.0001390228,0.00002018048,0.0003357625,0.00001868887,0.0001220301,7.553056e-7,0.0001541102,0.00001032042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006899895,"about_ca_system_score_gemma":0.0000117574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003297547,"about_ca_topic_score_gemma":0.00008741822,"domain_scores_codex":[0.9993082,0.0000210928,0.0002013888,0.0002082204,0.00008283867,0.0001782224],"domain_scores_gemma":[0.9996679,0.00005783928,0.00003110692,0.0001629573,0.00003506635,0.00004512506],"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":[0.00001486175,0.00002679392,0.0002021469,0.00002109625,0.00001229488,9.233257e-7,0.0001592189,0.9817479,0.0002098273,0.00001852204,0.00000266082,0.01758376],"study_design_scores_gemma":[0.0005661789,0.00008285905,0.0006426287,0.00003907136,0.0000125658,0.0000114134,0.00006440608,0.9974552,0.0009272715,0.000006966118,0.0000331052,0.0001583407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4368634,0.0000243026,0.5623908,0.000004574124,0.0001270234,0.0004049517,0.000004517489,0.0001115009,0.00006895724],"genre_scores_gemma":[0.9929841,0.00004857539,0.006737549,0.00001770997,0.00003253439,0.00009840165,0.00001738402,0.00003750407,0.00002622935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5561208,"threshold_uncertainty_score":0.5669184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004667069890156266,"score_gpt":0.1877470918303294,"score_spread":0.1830800219401731,"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."}}