{"id":"W4384927200","doi":"10.1016/j.comnet.2023.109937","title":"Optimal radio resource management in 5G NR featuring network slicing","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Slicing; Radio access network; Scheduling (production processes); Computer network; Leverage (statistics); Throughput; Distributed computing; Latency (audio); Mathematical optimization; Wireless; Base station; 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":[0.001442338,0.0007915288,0.001324294,0.0003936011,0.0005351344,0.001016067,0.0007457375,0.0006282856,0.002254548],"category_scores_gemma":[0.003290709,0.0004513689,0.0003351545,0.0005797316,0.0006858603,0.00111704,0.0008541258,0.000661923,0.0001640482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001301988,"about_ca_system_score_gemma":0.001972881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006465829,"about_ca_topic_score_gemma":0.008439898,"domain_scores_codex":[0.99915,0.0002984117,0.00003259039,0.0001566441,0.0001371271,0.0002252149],"domain_scores_gemma":[0.9986514,0.0008671841,0.0001256327,0.0001059689,0.0001532685,0.0000966229],"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":[0.0004432256,0.00008897213,0.0006923287,0.00007844601,0.00004139317,0.0001605105,0.00005739888,0.9300436,0.006993013,0.02260548,0.002432949,0.03636271],"study_design_scores_gemma":[0.00001021125,0.00003755603,0.0001251734,0.000005485733,0.000009255909,0.00001731178,0.00001487643,0.9947996,0.0005796052,0.004214938,0.0001810657,0.000005005962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1326082,0.00120665,0.8546345,0.0006044568,0.0002230824,0.0001064871,0.0001902525,0.0005246041,0.009901796],"genre_scores_gemma":[0.9451058,0.0002301155,0.05326758,0.00008973221,0.00007167905,0.00002386517,0.00003726523,0.00003304188,0.00114095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006465829,"threshold_uncertainty_score":0.01285636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278528794610561,"score_gpt":0.2204591113865803,"score_spread":0.2076738234404746,"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."}}