{"id":"W4386175394","doi":"10.1109/twc.2023.3306880","title":"DRL-Based Multidimensional Resource Management in SWIPT-NOMA-Enabled MEC","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Noma; Computer science; Wireless; Resource management (computing); Computer network; Telecommunications; Telecommunications link","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":[0.0004716314,0.000504643,0.0007706005,0.000249925,0.0004463971,0.0008338097,0.00088753,0.0005532512,0.0009401579],"category_scores_gemma":[0.001085295,0.0002484553,0.0003050559,0.0004079521,0.0005529175,0.0009532297,0.001133791,0.0005803666,0.0001976392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006007671,"about_ca_system_score_gemma":0.0007351415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002900745,"about_ca_topic_score_gemma":0.003292967,"domain_scores_codex":[0.9996377,0.0001049111,0.00001784617,0.00007959604,0.00008230639,0.00007758244],"domain_scores_gemma":[0.9996572,0.0001491903,0.00005498467,0.00004141667,0.00006254703,0.00003476131],"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":[0.00006751668,0.00003549698,0.0004312227,0.00006845237,0.00002430538,0.0001890514,0.0000609674,0.9481186,0.005710396,0.008228822,0.001187227,0.03587797],"study_design_scores_gemma":[0.000004199687,0.00001537693,0.00003585749,0.000002109299,0.000002586799,0.00002609552,0.000008999606,0.9979767,0.0004820062,0.001184107,0.0002588236,0.000003183948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03289171,0.0006527485,0.9600665,0.000294358,0.00006734314,0.00004742658,0.00005433072,0.0003129605,0.005612638],"genre_scores_gemma":[0.9357222,0.0002504062,0.06179407,0.0001390253,0.00001957952,0.00006597189,0.00004001764,0.00002384211,0.001944969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002900745,"threshold_uncertainty_score":0.005767643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651719128994878,"score_gpt":0.2639272732522934,"score_spread":0.2374100819623446,"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."}}