{"id":"W3210303814","doi":"10.1109/twc.2021.3122941","title":"Deep Reinforcement Learning-Based RAN Slicing for UL/DL Decoupled Cellular V2X","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Cellular network; Telecommunications link; Computer network; Quality of service; Base station; Ran; Throughput; Slicing; Distributed computing; Wireless; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001540198,0.0002401753,0.0002778201,0.0001799289,0.0006810839,0.00005861834,0.0004156674,0.0001356924,0.00008291697],"category_scores_gemma":[0.00001936941,0.000300263,0.0001768709,0.0004780981,0.00005907059,0.0001881742,0.000003799793,0.0004207417,0.00002917421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002611256,"about_ca_system_score_gemma":0.00007129539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001971139,"about_ca_topic_score_gemma":0.0004340621,"domain_scores_codex":[0.9987112,0.00009498782,0.0004999551,0.0002360608,0.0001500201,0.0003077402],"domain_scores_gemma":[0.9975889,0.0005217201,0.00009051865,0.001433663,0.0002616436,0.0001036064],"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":[0.00001240625,0.00008536803,0.000003543348,0.00007757242,0.00007188091,7.710944e-7,0.0003428865,0.9798312,0.01574655,0.0001584457,0.00002427015,0.003645113],"study_design_scores_gemma":[0.0009099041,0.00003734722,0.000001674701,0.0001101474,0.00006864239,0.000002523397,0.0003115181,0.9024025,0.09425537,0.00001921311,0.001614149,0.0002670706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001381868,0.0003679326,0.9958949,0.0002306098,0.000343782,0.0006009977,0.00001073395,0.0006215025,0.0005476542],"genre_scores_gemma":[0.9714977,0.0004610149,0.02645446,0.00006307152,0.00002568881,0.0007295273,0.000186044,0.00009964124,0.0004828888],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9701158,"threshold_uncertainty_score":0.9999449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789381004912911,"score_gpt":0.2479619839005147,"score_spread":0.2300681738513856,"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."}}