{"id":"W4387883701","doi":"10.1109/icc45041.2023.10278981","title":"Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Ran; Queueing theory; Reinforcement learning; Computer network; C-RAN; Artificial intelligence; Low latency (capital markets); Intelligent Network; Deep learning; Distributed computing; Real-time computing; Radio access network; Base station","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":[0.000285363,0.0001410608,0.0001510399,0.00007272093,0.0002125023,0.0001532258,0.0004913652,0.00005268634,0.0000432228],"category_scores_gemma":[0.00006059398,0.0001234124,0.0001065285,0.0005483582,0.00001483266,0.0002680902,0.0002265998,0.0001180236,0.0001838958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003991924,"about_ca_system_score_gemma":0.00002164902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001359074,"about_ca_topic_score_gemma":0.00001140634,"domain_scores_codex":[0.9987118,0.00001701698,0.0002537971,0.0003421228,0.0002126623,0.0004625762],"domain_scores_gemma":[0.9992358,0.0002884219,0.00005280158,0.0003087673,0.0000453666,0.00006884595],"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":[0.000004709239,0.000008413249,0.00003995702,0.00001786993,0.00001796252,0.000003593354,0.001596557,0.8676115,0.00001654464,0.02484357,0.0022753,0.103564],"study_design_scores_gemma":[0.0002263865,0.0001536997,0.000063441,0.00002094577,0.000004489722,0.000002639921,0.0002195962,0.9403878,0.0004242362,0.0007032942,0.05760978,0.0001837132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005324213,0.0001124512,0.9903834,0.0004421722,0.000565038,0.0002760882,1.091666e-7,0.001256716,0.001639802],"genre_scores_gemma":[0.9545997,0.0001576023,0.04021702,0.0004012789,0.000175982,0.000105529,0.00001360252,0.00002569993,0.004303549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9501664,"threshold_uncertainty_score":0.5032612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03780339781618514,"score_gpt":0.2707132354695981,"score_spread":0.2329098376534129,"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."}}