{"id":"W4312853625","doi":"10.1109/lcomm.2022.3214146","title":"Joint Lifetime-Outage Optimization in Relay-Enabled IoT Networks—A Deep Reinforcement Learning Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Relay; Benchmark (surveying); Reinforcement learning; Maximization; Joint (building); Internet of Things; Scheme (mathematics); Base station; Selection (genetic algorithm); Optimization problem; Transmission (telecommunications); Computer network; Nonlinear programming; Mathematical optimization; Nonlinear system; Artificial intelligence; Telecommunications; Algorithm; Power (physics); Engineering","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":[0.001350806,0.0006982753,0.0009929867,0.0003171324,0.0002790808,0.0005967253,0.0009953779,0.001033079,0.001106799],"category_scores_gemma":[0.002656792,0.0003373583,0.0003156667,0.0003474968,0.0008849229,0.001170282,0.0009506493,0.001014273,0.00009304882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151901,"about_ca_system_score_gemma":0.001021282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00502004,"about_ca_topic_score_gemma":0.003867635,"domain_scores_codex":[0.9996552,0.0001331555,0.00001236402,0.00006551648,0.0000587524,0.00007490606],"domain_scores_gemma":[0.998723,0.0008616186,0.0001355266,0.00003895064,0.0001580748,0.00008284829],"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":[0.00003479769,0.00002322274,0.0003011172,0.00002242349,0.00001433473,0.00004260575,0.00001862209,0.9867195,0.0004393689,0.004985723,0.000333725,0.007064501],"study_design_scores_gemma":[0.00000227823,0.000008824908,0.00002359176,0.000001334949,0.000001808673,0.000003651962,0.000002906363,0.9984348,0.00005459712,0.001422258,0.00004278982,0.000001155883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07707026,0.0007137274,0.9172999,0.0007352916,0.00004973764,0.00004470425,0.00005940011,0.0001667192,0.003860135],"genre_scores_gemma":[0.975229,0.0002400918,0.02236771,0.0001225292,0.00002675414,0.00004158401,0.0000389335,0.00002471139,0.001908649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00502004,"threshold_uncertainty_score":0.009981692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03643706991205457,"score_gpt":0.251818523258253,"score_spread":0.2153814533461984,"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."}}