{"id":"W4389104987","doi":"10.1109/jsac.2023.3336156","title":"Flexible RAN Slicing in Open RAN With Constrained Multi-Agent Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Reinforcement learning; Computer science; Distributed computing; Leverage (statistics); Provisioning; Exploit; Software deployment; Slicing; Computer network; Artificial intelligence; Computer security; World Wide Web; Software engineering","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":[0.001065288,0.0007095541,0.0007813921,0.0002406885,0.0003377605,0.0006568387,0.001073814,0.0007332038,0.001115018],"category_scores_gemma":[0.003226401,0.0003838465,0.0003658848,0.0001765316,0.0008883635,0.0008354596,0.000971397,0.001153189,0.0001721912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005736052,"about_ca_system_score_gemma":0.001041392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005864307,"about_ca_topic_score_gemma":0.005858094,"domain_scores_codex":[0.9995752,0.0001316978,0.00001933662,0.0001114106,0.00007183619,0.00009059131],"domain_scores_gemma":[0.9982759,0.001027414,0.0002471268,0.0001219053,0.0001963901,0.0001312955],"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":[0.00004475862,0.00003044447,0.0005042623,0.00001906295,0.00001539554,0.00005340399,0.00003235716,0.9836673,0.001119204,0.001989944,0.000238827,0.01228517],"study_design_scores_gemma":[0.000003949863,0.00001288297,0.00003654764,0.000001472905,0.000002059774,0.000004282195,0.000003043115,0.9991193,0.0001421427,0.0006007747,0.00007196207,0.000001632214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0578282,0.0002057514,0.9388966,0.0001677044,0.0000413454,0.00005110848,0.00002815349,0.0004614238,0.002319706],"genre_scores_gemma":[0.9441938,0.00006525385,0.05433307,0.000102818,0.00001742733,0.00006468396,0.00004455226,0.00003445182,0.001143941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005864307,"threshold_uncertainty_score":0.01166034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07500392290630888,"score_gpt":0.3300656627497989,"score_spread":0.2550617398434901,"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."}}