{"id":"W4285126844","doi":"10.1109/tnse.2022.3176924","title":"Hybrid NOMA-FDMA Assisted Dual Computation Offloading: A Latency Minimization Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Noma; Computer science; Minification; Dual (grammatical number); Computation; Computer network; Latency (audio); Telecommunications link; Algorithm; Telecommunications","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":[0.0005413244,0.001032705,0.0008260262,0.0003432277,0.0004745579,0.001031214,0.001407085,0.0007378096,0.001968306],"category_scores_gemma":[0.0009179242,0.0003550336,0.0005352263,0.0005993387,0.0006413351,0.0008097351,0.001305798,0.000822527,0.0004143825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013354,"about_ca_system_score_gemma":0.001095615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00312128,"about_ca_topic_score_gemma":0.004197011,"domain_scores_codex":[0.9995686,0.00009383666,0.00001578904,0.00008351229,0.000116099,0.0001220513],"domain_scores_gemma":[0.9995283,0.0002375549,0.0000555251,0.00005312363,0.00007780296,0.00004767179],"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":[0.0001496346,0.0001114749,0.0005446066,0.00008774468,0.0000415523,0.0001415674,0.00009972746,0.9135899,0.01053115,0.01623412,0.001731677,0.05673688],"study_design_scores_gemma":[0.000006978489,0.00002579694,0.00003765396,0.000002676842,0.000004475447,0.00001879992,0.00001415627,0.9970866,0.0005843802,0.001874896,0.0003400534,0.000003508763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01580922,0.0002682272,0.9802484,0.0001684821,0.00005107117,0.00004270666,0.00003120948,0.0001435365,0.003237171],"genre_scores_gemma":[0.7695251,0.0003738319,0.2237474,0.0001636696,0.00008751568,0.0001735915,0.00009368097,0.0000620146,0.005773149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00312128,"threshold_uncertainty_score":0.006584585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484589780778081,"score_gpt":0.2029269105929076,"score_spread":0.1880810127851268,"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."}}