{"id":"W3047617695","doi":"10.1109/tcomm.2020.3014939","title":"Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Department for Business, Energy and Industrial Strategy, UK Government; Queen's University Belfast; Royal Society; Royal Academy of Engineering; Newton Fund","keywords":"Computer science; Throughput; Telecommunications link; Resource allocation; Quality of service; Transmitter power output; Context (archaeology); Computer network; Resource management (computing); Efficient energy use; Distributed computing; Transmitter; Real-time computing; Wireless; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001028753,0.0002289273,0.0002103328,0.0001308351,0.0009221661,0.00009054782,0.0009259737,0.0001383636,0.0000199449],"category_scores_gemma":[0.00001009554,0.0002657723,0.0001059751,0.0006411044,0.0002215755,0.0001421938,0.00001779419,0.0002884239,0.00002272718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001098957,"about_ca_system_score_gemma":0.00003973769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003434487,"about_ca_topic_score_gemma":0.000145583,"domain_scores_codex":[0.9988186,0.00009044253,0.000445466,0.0002816496,0.0001407114,0.0002230856],"domain_scores_gemma":[0.9970388,0.0004258797,0.00008096718,0.002130466,0.0001563092,0.0001675386],"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":[0.00005826932,0.0008944215,0.00001195216,0.0001232859,0.0002955849,2.152911e-7,0.003860302,0.7825317,0.00767503,0.03303387,0.005402046,0.1661133],"study_design_scores_gemma":[0.0004797882,0.00006125818,0.00008323717,0.0000258787,0.00009589009,0.000004106431,0.0003824722,0.8929738,0.001845159,0.0001606834,0.1036225,0.0002652415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006978115,0.0008093758,0.9814118,0.01147948,0.00006358791,0.00063957,0.0001907198,0.0007011296,0.004006579],"genre_scores_gemma":[0.9394333,0.001970333,0.05652238,0.0005135763,0.00002953661,0.001123398,0.0002263706,0.00006976612,0.0001113863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9387354,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04019573114552203,"score_gpt":0.2487136217206766,"score_spread":0.2085178905751545,"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."}}