{"id":"W4380303742","doi":"10.1109/tvt.2023.3285069","title":"Multi-User Dynamic Computation Offloading and Resource Allocation in 5G MEC Heterogeneous Networks with Static and Dynamic Subchannels","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computation offloading; Computer science; Mobile edge computing; Lyapunov optimization; Server; Base station; Computer network; Distributed computing; User equipment; Energy consumption; Resource allocation; Wireless; Queuing delay; Wireless network; Queueing theory; Edge computing; Enhanced Data Rates for GSM Evolution; Engineering","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":[0.0002289462,0.0001993557,0.0002198905,0.0008743958,0.0002440983,0.00007522215,0.000227864,0.0002058094,2.241709e-7],"category_scores_gemma":[0.000004597359,0.0001992736,0.00002709531,0.001349537,0.0001173115,0.0001644638,0.00001108788,0.0003679902,0.00000693218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000936306,"about_ca_system_score_gemma":0.0000227448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000296378,"about_ca_topic_score_gemma":0.0001533211,"domain_scores_codex":[0.9986173,0.00007485247,0.0002517989,0.0005345461,0.0001386528,0.0003828394],"domain_scores_gemma":[0.9994432,0.0001046514,0.00007848618,0.0002739053,0.0000455509,0.00005421192],"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":[0.00001824329,0.00006976984,0.0001032284,0.00003652255,0.00004350878,0.0001001172,0.0006255461,0.8672147,0.002637617,0.00004932962,0.000007020917,0.1290943],"study_design_scores_gemma":[0.0006504955,0.0001804084,0.0007135139,0.0000924972,0.0000150235,0.0001494634,0.00009180864,0.996392,0.001212136,0.0002409586,0.00004968615,0.0002119758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4324806,0.00007171182,0.5660611,0.0005774267,0.0002489368,0.0001817452,3.093403e-7,0.0003770114,0.000001223885],"genre_scores_gemma":[0.9880688,0.00006495674,0.01167717,0.00006736506,0.00001088794,0.00004242424,0.000004931095,0.00002492668,0.00003858141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5555882,"threshold_uncertainty_score":0.8126141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008578188534546642,"score_gpt":0.2350261626355015,"score_spread":0.2264479741009549,"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."}}