{"id":"W4406948936","doi":"10.1109/lcomm.2025.3536182","title":"Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Joint (building); Reinforcement; Computer network; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002878839,0.000108014,0.0001267882,0.0003499885,0.0002405883,0.0001104607,0.001405443,0.00004386814,0.000007367453],"category_scores_gemma":[0.00003276072,0.0001194443,0.00003214365,0.0005662408,0.0001153829,0.0001966685,0.0009241719,0.0001774096,0.000024899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358718,"about_ca_system_score_gemma":0.00001441131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007501299,"about_ca_topic_score_gemma":0.000008368603,"domain_scores_codex":[0.9991213,0.00006906293,0.000278962,0.0002308633,0.00009018633,0.0002095935],"domain_scores_gemma":[0.9982142,0.0001355513,0.00008598192,0.001506752,0.0000383576,0.00001917347],"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":[0.00001481862,0.0001963184,0.001154335,0.0001219963,0.0002958598,0.000007025332,0.001805012,0.3777512,0.007658117,0.2769566,0.04298225,0.2910565],"study_design_scores_gemma":[0.0005917336,0.00003146194,0.001087125,0.00009076481,0.00001142786,0.000001355091,0.0000698026,0.9646608,0.0007445849,0.001806052,0.03070308,0.0002017619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001284643,0.0003870964,0.961646,0.03218954,0.00008915379,0.0005347601,1.36443e-7,0.0002770091,0.003591654],"genre_scores_gemma":[0.7978008,0.0003266866,0.1973984,0.00303559,0.000005889903,0.0002879453,0.00001167074,0.00001111299,0.00112191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7965161,"threshold_uncertainty_score":0.4870796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479070572466569,"score_gpt":0.250461686174553,"score_spread":0.2356709804498873,"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."}}