{"id":"W3190893732","doi":"10.1109/icc42927.2021.9500285","title":"Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Noma; Reinforcement learning; Computer science; Internet of Things; Network packet; Latency (audio); Power (physics); Low latency (capital markets); Mathematical optimization; Algorithm; Artificial intelligence; Computer network; Mathematics; Telecommunications","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":[0.00003806282,0.00005922235,0.00008828552,0.00003512534,0.00003103101,0.00001153612,0.00006315292,0.00004533807,0.00001564415],"category_scores_gemma":[0.00003430632,0.00006449765,0.00001151774,0.0001008886,0.00002231474,0.00003603999,0.00007121915,0.0001381947,0.000001100693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003467201,"about_ca_system_score_gemma":0.000003509151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002418316,"about_ca_topic_score_gemma":0.00003524691,"domain_scores_codex":[0.9996683,0.000006317157,0.0001085795,0.00007499129,0.00002700937,0.0001148024],"domain_scores_gemma":[0.9997075,0.000109833,0.00001154982,0.0001332972,0.00002532186,0.00001245683],"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":[0.000001046284,0.00000251191,0.00020157,0.00001006464,0.000006316647,0.000001125632,0.00004478543,0.9350029,0.0002187765,0.02389111,0.00002123626,0.04059851],"study_design_scores_gemma":[0.0002628576,0.0000148611,0.0005370881,0.00002401028,0.000001014214,0.000001798198,0.0009629231,0.9798431,0.00648223,0.0002860724,0.01149371,0.00009036725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005753239,0.001179467,0.9866469,0.0001573961,0.00003104594,0.0001146871,2.472709e-7,0.0003979408,0.005719075],"genre_scores_gemma":[0.9572234,0.0008688332,0.04152904,0.00002754612,0.000007433983,0.0000509601,0.00001179972,0.00001132362,0.000269646],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9514702,"threshold_uncertainty_score":0.2630138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221847738290555,"score_gpt":0.2376414456581873,"score_spread":0.2254229682752818,"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."}}