{"id":"W4414458973","doi":"10.1109/lnet.2025.3614549","title":"DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Provisioning; Latency (audio); Resource allocation; Bandwidth (computing); Edge device; Edge computing; Enhanced Data Rates for GSM Evolution; Virtual reality; Bandwidth allocation","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":[0.001046265,0.000624667,0.000798969,0.0002707209,0.0003431195,0.0007931749,0.001041403,0.0005452096,0.001359304],"category_scores_gemma":[0.00168813,0.0003223194,0.0002397806,0.000305848,0.0005596463,0.001000758,0.0009959674,0.0006926308,0.0001664719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001330765,"about_ca_system_score_gemma":0.0009390697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005880048,"about_ca_topic_score_gemma":0.007272455,"domain_scores_codex":[0.99964,0.0001190898,0.00001060122,0.00006780461,0.00006459591,0.00009797143],"domain_scores_gemma":[0.9995098,0.0002354732,0.00006119104,0.00002720422,0.0001128617,0.00005342968],"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":[0.00007297999,0.00003546028,0.000444295,0.00002393149,0.00001537051,0.00004702946,0.00002760978,0.975323,0.001907147,0.004441667,0.0007769973,0.01688439],"study_design_scores_gemma":[0.000002096006,0.000008595486,0.00002496111,9.821536e-7,0.000001491304,0.000004632621,0.000004026399,0.9989734,0.0001408528,0.0007605352,0.00007730893,0.000001280786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05241721,0.000581493,0.9432809,0.0003178192,0.00004112408,0.00004208101,0.00005021497,0.0002330016,0.003036193],"genre_scores_gemma":[0.9621464,0.0001574938,0.03605842,0.0001103428,0.00002161577,0.0000259227,0.00004308396,0.0000401065,0.001396621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005880048,"threshold_uncertainty_score":0.01169169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0339492793363138,"score_gpt":0.273324640407439,"score_spread":0.2393753610711252,"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."}}