{"id":"W2615926310","doi":"10.1109/twc.2017.2703901","title":"Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":719,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Computation offloading; Mobile edge computing; Resource allocation; Distributed computing; Optimization problem; Wireless network; Wireless; Cellular network; Enhanced Data Rates for GSM Evolution; Computation; Convex optimization; Edge computing; Computer network; Mathematical optimization; Server; Regular polygon; Algorithm; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004030471,0.0006573247,0.0007011099,0.000350242,0.000760318,0.0009262229,0.0006987919,0.0006044754,0.0005953917],"category_scores_gemma":[0.001138646,0.0002114849,0.0002644286,0.001031728,0.0006737842,0.0006495203,0.0007228674,0.0005321735,0.0001066734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001172604,"about_ca_system_score_gemma":0.001075162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01151065,"about_ca_topic_score_gemma":0.01287507,"domain_scores_codex":[0.9995493,0.0001736198,0.00001413396,0.00005692229,0.0000977548,0.0001082487],"domain_scores_gemma":[0.9996024,0.00022256,0.00004023538,0.00004041887,0.00006326196,0.00003111774],"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":[0.000132476,0.00007205409,0.0008600637,0.00006042357,0.00002405137,0.0002374457,0.00006544191,0.9180789,0.004250128,0.01808318,0.001820941,0.05631487],"study_design_scores_gemma":[0.000004822856,0.0000126074,0.00007604365,0.0000019544,0.000004004821,0.00001662999,0.00001402936,0.996173,0.0004680954,0.002907448,0.0003186853,0.000002804596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1217546,0.001754514,0.8661463,0.0006917605,0.0001147544,0.00009507885,0.00004891066,0.000286978,0.00910707],"genre_scores_gemma":[0.9631329,0.0004437863,0.03475992,0.0000791931,0.00003824937,0.00004897158,0.0000234177,0.00001758205,0.001455909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01151065,"threshold_uncertainty_score":0.02288729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02450027118758435,"score_gpt":0.2558669389784726,"score_spread":0.2313666677908882,"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."}}