{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002581007,0.000308467,0.0002989874,0.001103497,0.0004732197,0.0000328109,0.001623489,0.0001874816,0.00003975869],"category_scores_gemma":[0.000006871355,0.0003623854,0.0001260971,0.002209603,0.0002502575,0.000194267,0.00003435578,0.0008586462,0.0003456192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003104621,"about_ca_system_score_gemma":0.00002909215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002612852,"about_ca_topic_score_gemma":0.0002788723,"domain_scores_codex":[0.9982308,0.000150742,0.0005549078,0.0003120251,0.0002917686,0.0004597447],"domain_scores_gemma":[0.9955177,0.0006466019,0.00006814657,0.00362548,0.00005878467,0.0000832564],"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":[0.00001832435,0.0002538561,0.0000191635,0.00004440163,0.00007006797,0.000006854088,0.0001762597,0.9268097,0.002492278,0.00361176,0.0004043946,0.06609289],"study_design_scores_gemma":[0.001558844,0.00003781946,0.0008132409,0.0002481233,0.00003164048,0.000003545662,0.001329207,0.9448716,0.02669096,0.0006855718,0.02307477,0.0006546631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08788414,0.0005411529,0.886281,0.004631973,0.0003611008,0.001542446,0.0001309174,0.01010515,0.008522118],"genre_scores_gemma":[0.9764426,0.002122014,0.01965848,0.0001013917,0.000004897351,0.001143935,0.00007622748,0.00009473122,0.0003557568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8885584,"threshold_uncertainty_score":0.9998828,"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."}}