{"id":"W2807209692","doi":"10.1109/tcomm.2019.2906622","title":"UAV-Enabled Communication Using NOMA","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Computational Science and Technology; Queen's University; Queen's University Belfast; British Council; Royal Society; Australian Research Council; King Fahd University of Petroleum and Minerals; Royal Academy of Engineering; Newton Fund; National Science Foundation","keywords":"Beamwidth; Computer science; Optimization problem; Base station; Transmitter power output; Dirty paper coding; Convex optimization; Wireless; Mathematical optimization; Bandwidth (computing); Real-time computing; Computer network; Algorithm; Antenna (radio); Telecommunications; Channel (broadcasting); MIMO; Regular polygon; Mathematics; Precoding","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004105805,0.0007519557,0.0005106783,0.0004027501,0.0005114748,0.00079183,0.0004915916,0.0004420233,0.001187375],"category_scores_gemma":[0.001348374,0.0001810452,0.0003455718,0.0005343581,0.0003740045,0.0008386528,0.001123328,0.0005865789,0.0003426977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003516575,"about_ca_system_score_gemma":0.000558592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002436916,"about_ca_topic_score_gemma":0.003802559,"domain_scores_codex":[0.999559,0.0001834445,0.00001858583,0.0000586074,0.00008838759,0.00009195282],"domain_scores_gemma":[0.999413,0.0002850167,0.0000818792,0.00009351976,0.00009361434,0.00003298355],"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":[0.0002027365,0.00006735535,0.001090428,0.0001908211,0.00007733457,0.0003684644,0.0001005015,0.8591647,0.0108104,0.02656378,0.003144396,0.09821922],"study_design_scores_gemma":[0.00001147821,0.0000870111,0.0002145139,0.00001358393,0.00001327498,0.00008125237,0.00002682996,0.9909925,0.00202798,0.004056183,0.0024653,0.0000101835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08533742,0.001712483,0.8945482,0.0003122976,0.0002358192,0.00006402901,0.000169181,0.0005057803,0.01711478],"genre_scores_gemma":[0.9367764,0.0006539162,0.05947275,0.00009750783,0.0000636078,0.0000718809,0.0001270195,0.00002125845,0.002715502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002436916,"threshold_uncertainty_score":0.0048455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03897177894309839,"score_gpt":0.267670380773226,"score_spread":0.2286986018301276,"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."}}