{"id":"W3030155047","doi":"10.1109/ojvt.2020.3032844","title":"Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting","year":2020,"lang":"en","type":"preprint","venue":"IEEE Open Journal of Vehicular Technology","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Khalifa University of Science, Technology and Research","keywords":"Telecommunications link; Space-division multiple access; Computer science; Base station; Computer network; Cellular network; Noma; Scheme (mathematics); Wireless; Multiplexing; Key (lock); Distributed computing; Real-time computing; Telecommunications; Computer security; Mathematics","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":[0.0004038716,0.0002906288,0.0007009999,0.0004273964,0.00005826351,0.0003064843,0.001352884,0.0006086728,0.00002380379],"category_scores_gemma":[0.00009928937,0.0003014578,0.00008252863,0.0005274529,0.00005059293,0.0002274521,0.001035835,0.001336733,0.000004860774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009543253,"about_ca_system_score_gemma":0.0001115623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000540352,"about_ca_topic_score_gemma":0.0001666538,"domain_scores_codex":[0.9983112,0.00004739717,0.000850611,0.0003736732,0.0001463833,0.0002707792],"domain_scores_gemma":[0.9989527,0.00007811407,0.0003350903,0.0003313926,0.0001620238,0.0001407155],"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":[0.00008172609,0.00003366469,0.008874321,0.00003181244,0.0001299177,0.00009981367,0.00006870589,0.9791856,0.005295387,0.00006862071,0.0003302858,0.005800184],"study_design_scores_gemma":[0.002365196,0.0001346562,0.01980411,0.0007551111,0.0001374721,0.00007310742,0.0001061177,0.962014,0.006313505,0.005333239,0.002191141,0.0007723813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840983,0.0004497097,0.2130407,0.001031247,0.0006296882,0.0006034095,0.00003444409,0.00005392683,0.00005855758],"genre_scores_gemma":[0.9720856,0.000411823,0.02677678,0.00009485429,0.0004393501,0.0000884298,0.00004314275,0.00005749878,0.00000250362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1879873,"threshold_uncertainty_score":0.9999437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01912731090845348,"score_gpt":0.2704567359442387,"score_spread":0.2513294250357853,"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."}}