{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008857981,0.00118721,0.001024421,0.000344715,0.0005164738,0.001437761,0.001079845,0.0007846932,0.001624338],"category_scores_gemma":[0.00117036,0.0003679346,0.0005435391,0.0008050523,0.0008981246,0.001560235,0.001042956,0.0006089198,0.0001868946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001621003,"about_ca_system_score_gemma":0.0009823901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004207797,"about_ca_topic_score_gemma":0.003253097,"domain_scores_codex":[0.9993426,0.0002956295,0.00001327736,0.0001247986,0.00009150397,0.0001322652],"domain_scores_gemma":[0.9997118,0.0001769075,0.0000431632,0.00001754778,0.00002669779,0.00002388719],"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":[0.00004212057,0.00002620394,0.0003428481,0.00004193588,0.00002393028,0.0001512674,0.00002211847,0.9593455,0.0009191595,0.02771776,0.000686648,0.01068046],"study_design_scores_gemma":[0.000005579823,0.00001790275,0.0001018578,0.000003079435,0.000006370881,0.00002543113,0.00002131682,0.9899647,0.0002120428,0.009178283,0.0004598518,0.000003626755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05577005,0.0009362977,0.9351203,0.0004735705,0.00005490937,0.00006298447,0.0001220656,0.0001109203,0.007348941],"genre_scores_gemma":[0.9377939,0.0006907808,0.05879257,0.0001069455,0.00005769402,0.00006763532,0.00009175228,0.00003522587,0.002363526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004207797,"threshold_uncertainty_score":0.01176131,"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."}}