{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034404,0.001108302,0.00161065,0.0004900593,0.0006336944,0.000979292,0.001294501,0.00152714,0.001951716],"category_scores_gemma":[0.005540593,0.000414699,0.0005381423,0.0005569373,0.001621518,0.001027678,0.001088485,0.001531419,0.0002409709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028389,"about_ca_system_score_gemma":0.001355549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005597824,"about_ca_topic_score_gemma":0.003975648,"domain_scores_codex":[0.9991496,0.0004199105,0.00003045214,0.0001192945,0.0001448829,0.0001359062],"domain_scores_gemma":[0.9965624,0.002620518,0.0002691858,0.00009014513,0.0003076151,0.000150191],"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":[0.00004120723,0.0000492652,0.0004122891,0.0000445661,0.00003829452,0.00004492047,0.00002893068,0.9690257,0.0003044509,0.01715901,0.0005523564,0.01229909],"study_design_scores_gemma":[0.000007259785,0.00001467389,0.00003319063,0.00000234564,0.000002780569,0.00000503383,0.000003903664,0.99547,0.00003875984,0.004304765,0.0001152503,0.000002091103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02933354,0.0009813397,0.9629985,0.0006081671,0.0001200885,0.00006119462,0.00002892198,0.0001445665,0.005723686],"genre_scores_gemma":[0.9397888,0.0003982525,0.05638009,0.0002875004,0.0001144102,0.000137345,0.00004071305,0.00003494861,0.002818067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005597824,"threshold_uncertainty_score":0.01113051,"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."}}