{"id":"W4285794073","doi":"10.3390/s22145375","title":"Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Ran; Radio access network; Orchestration; C-RAN; Computer science; Virtualization; Cloud computing; Software deployment; Computer network; Base station; Operating system","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":[0.001897685,0.0008796355,0.001316346,0.0003148915,0.0007682164,0.001695233,0.001260482,0.001580233,0.001527874],"category_scores_gemma":[0.005293274,0.0004004162,0.0007234845,0.000299869,0.001520849,0.001210355,0.002235384,0.001667716,0.0002298017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007533808,"about_ca_system_score_gemma":0.001239321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003954513,"about_ca_topic_score_gemma":0.002118191,"domain_scores_codex":[0.9988831,0.0005338154,0.00004644554,0.0002337498,0.000126588,0.0001763474],"domain_scores_gemma":[0.9968227,0.002111118,0.0003927815,0.0001370498,0.0002220737,0.0003142454],"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":[0.00006945868,0.00007068706,0.0009787979,0.00007556779,0.00007096175,0.00016472,0.000145858,0.9564179,0.0005801832,0.02757998,0.0006849875,0.01316097],"study_design_scores_gemma":[0.00002118329,0.00005351315,0.0001035284,0.00001139622,0.000008740057,0.00001642881,0.00003482941,0.984884,0.0001370245,0.01410016,0.0006223705,0.000006775301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06258612,0.000938953,0.9270609,0.001086832,0.0001505592,0.0000987559,0.00004254403,0.0002022608,0.007833131],"genre_scores_gemma":[0.9140626,0.0005573333,0.08096647,0.0003668717,0.0001300801,0.0002484035,0.00006296372,0.00003876726,0.003566578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003954513,"threshold_uncertainty_score":0.01003599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210734865444499,"score_gpt":0.2785335052848206,"score_spread":0.2564261566303757,"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."}}