{"id":"W4412505250","doi":"10.1002/ett.70210","title":"Multi‐Objective Resource Optimization in <scp>UAV</scp>‐Enabled Heterogeneous Cellular Networks Using Serverless Federated Learning and Power‐Domain <scp>NOMA</scp>","year":2025,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Anhui Provincial Department of Education; Anyang Institute of Technology; Henan University","keywords":"Noma; Computer science; Computer network; Domain (mathematical analysis); Distributed computing; Telecommunications link","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002898999,0.000387753,0.0003668374,0.001118758,0.001167436,0.0001861999,0.0005331696,0.0004605646,0.000005984978],"category_scores_gemma":[0.0001435909,0.0004684334,0.00008629591,0.002561281,0.0001787097,0.0002626219,0.00006304021,0.001061508,0.000003456322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003528125,"about_ca_system_score_gemma":0.00004104013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006388236,"about_ca_topic_score_gemma":0.0001792803,"domain_scores_codex":[0.9981418,0.0001674936,0.0005963132,0.0004635464,0.0001290122,0.0005018571],"domain_scores_gemma":[0.9983088,0.0005516176,0.0001392393,0.0008177301,0.0001367075,0.00004585693],"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":[0.000002246399,0.0001365156,0.0002824382,0.00002864388,0.00009521646,0.000001394452,0.0004609265,0.9933656,0.001849695,0.0001244533,0.00002923123,0.003623656],"study_design_scores_gemma":[0.0005902525,0.00003453503,0.00016388,0.0001391278,0.00005398344,0.000006954939,0.008526264,0.9810861,0.006997568,0.0001171957,0.00215723,0.0001269374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1524983,0.002446888,0.8409914,0.0001405138,0.00005814725,0.000642558,0.000008293625,0.002364442,0.0008495176],"genre_scores_gemma":[0.8753344,0.002763483,0.1212262,0.00001854381,0.000004298683,0.0003302532,0.00006820857,0.00007577867,0.00017881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7228362,"threshold_uncertainty_score":0.9997767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008079918845824872,"score_gpt":0.2268997029849879,"score_spread":0.2188197841391631,"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."}}