{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009986032,0.001053699,0.0007722927,0.000426075,0.0005236391,0.00113524,0.001049018,0.0006794397,0.001557519],"category_scores_gemma":[0.001468648,0.0003202497,0.0004916452,0.0006790108,0.0008056186,0.001094885,0.0007837677,0.000728622,0.0002536084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261039,"about_ca_system_score_gemma":0.001266118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003309723,"about_ca_topic_score_gemma":0.003418513,"domain_scores_codex":[0.9994035,0.0002247155,0.00001921489,0.0001040756,0.0001530773,0.00009542285],"domain_scores_gemma":[0.9996434,0.0002032461,0.00005655859,0.00002548761,0.00005111022,0.00002004418],"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":[0.00003205221,0.00002889795,0.0001494243,0.00003885581,0.00001998037,0.00005357007,0.00002980497,0.9641649,0.00178637,0.02323729,0.0005607145,0.00989804],"study_design_scores_gemma":[0.000003298142,0.00002238614,0.00006832876,0.000003977704,0.00000461313,0.00001503683,0.00001195941,0.9951093,0.00030277,0.003988292,0.0004662826,0.000003897782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01051193,0.0004274673,0.9850679,0.0001147945,0.00003608811,0.00004373154,0.00004035791,0.00005632318,0.003701314],"genre_scores_gemma":[0.9025317,0.0007049885,0.09114263,0.00007827149,0.00008302132,0.0001885446,0.00005882352,0.00005504997,0.005156889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003309723,"threshold_uncertainty_score":0.009149551,"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."}}