{"id":"W4388040554","doi":"10.1109/pimrc56721.2023.10293819","title":"Multi-Connectivity Mobility Management in Downlink FD-RAN: A Learning Based Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Telecommunications link; Handover; Computer science; Computer network; User equipment; Radio access network; Reinforcement learning; Base station; Quality of service; Cellular network; Mobility management; Ran; C-RAN; Real-time computing; Artificial intelligence; Mobile 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006523457,0.0005842664,0.0007451525,0.0002541159,0.0003352207,0.0006216671,0.001120338,0.0008131791,0.001012646],"category_scores_gemma":[0.001344298,0.0002685784,0.0003183906,0.0002794339,0.0007859864,0.0007892667,0.000902521,0.0008560728,0.0001162465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008018421,"about_ca_system_score_gemma":0.0008742452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006270874,"about_ca_topic_score_gemma":0.004589387,"domain_scores_codex":[0.9996736,0.00009868723,0.00001199694,0.00007534094,0.00005964326,0.00008077917],"domain_scores_gemma":[0.9993858,0.0002997455,0.00009111875,0.00004040572,0.0001258005,0.00005702768],"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":[0.0000413218,0.00005865819,0.0005552819,0.00002255192,0.00001976608,0.00005512902,0.00002237928,0.9747019,0.001223016,0.003170168,0.0003114079,0.01981843],"study_design_scores_gemma":[0.000001614852,0.00001018722,0.0000324529,7.444032e-7,0.000001722574,0.000003496734,0.000002091409,0.9994971,0.00006405307,0.0003477678,0.00003784411,9.015108e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05704981,0.0003746831,0.9390253,0.00041889,0.00003954275,0.0000346062,0.00002321831,0.0001545823,0.002879393],"genre_scores_gemma":[0.9618779,0.0001723804,0.03609212,0.0001102009,0.00004356893,0.00003302383,0.00002953554,0.00001382445,0.001627492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006270874,"threshold_uncertainty_score":0.01246876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01886778684849566,"score_gpt":0.2412591738641335,"score_spread":0.2223913870156379,"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."}}