{"id":"W4210349797","doi":"10.1155/2022/4827956","title":"Game-Based Channel Selection for UAV Services in Mobile Edge Computing","year":2022,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Beijing Nova Program; Beijing Municipal Natural Science Foundation; Beijing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Computer science; Base station; Channel (broadcasting); Transmission (telecommunications); Enhanced Data Rates for GSM Evolution; Selection (genetic algorithm); Computer network; Mobile edge computing; Nash equilibrium; Selection algorithm; Computation; Distributed computing; Game theory; Mathematical optimization; Server; Algorithm; Telecommunications; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.000243039,0.00007274979,0.00008670498,0.00006332274,0.0002877334,0.00003296174,0.000156596,0.00004643759,0.00001108267],"category_scores_gemma":[9.374106e-7,0.00009377257,0.00002026307,0.000300651,0.00001432582,0.0000676386,0.00006419971,0.0002060039,3.499891e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005431976,"about_ca_system_score_gemma":0.000006591812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004081192,"about_ca_topic_score_gemma":0.0002108528,"domain_scores_codex":[0.9995137,0.00005553816,0.0001592209,0.0001027375,0.00004810293,0.0001207307],"domain_scores_gemma":[0.999627,0.00008163482,0.00004136433,0.0001968384,0.00003062611,0.00002251381],"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":[0.000007949893,0.00003788495,0.0003367046,0.00003910589,0.000004198816,1.387901e-8,0.0009566917,0.9936876,0.000006343244,0.0005246321,0.00008716426,0.004311652],"study_design_scores_gemma":[0.0003203242,0.00002589889,0.0002975798,0.0000160765,0.000005370803,8.527531e-7,0.0004354512,0.9904032,0.00001736988,0.0008527123,0.007531034,0.00009408869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5179381,0.01179133,0.4661552,0.0003170694,0.0001950492,0.001855923,0.00003267267,0.0005798018,0.00113482],"genre_scores_gemma":[0.9972332,0.0004492072,0.001288378,0.0001023297,0.0000302785,0.0005241219,0.0003528122,0.00001526448,0.00000439855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4792951,"threshold_uncertainty_score":0.3823934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005224125387027833,"score_gpt":0.207488682058737,"score_spread":0.2022645566717091,"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."}}