{"id":"W4394586210","doi":"10.1109/tmlcn.2024.3385748","title":"Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; Canada Research Chairs","keywords":"Noma; Reinforcement learning; Reinforcement; Computer science; Artificial intelligence; Telecommunications; Engineering; Telecommunications link; Structural 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005411643,0.0002644559,0.0002609655,0.0004426859,0.0007679508,0.0001542838,0.0006095699,0.0001689942,0.000006042207],"category_scores_gemma":[0.000009867898,0.0002895471,0.00008230515,0.0006074284,0.0001091723,0.0002052604,0.00001216212,0.001354075,0.000004678073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002884373,"about_ca_system_score_gemma":0.00002916822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003921916,"about_ca_topic_score_gemma":0.0002676031,"domain_scores_codex":[0.9985478,0.0002872573,0.0004579442,0.0002737305,0.0001167202,0.0003165527],"domain_scores_gemma":[0.9974371,0.001385781,0.00006904547,0.001000806,0.0000451864,0.00006206087],"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":[0.00001985782,0.00003060008,0.00002896845,0.0001180601,0.00003576902,8.192761e-7,0.0001884116,0.8158892,0.0001567091,0.0001815155,0.00000472682,0.1833454],"study_design_scores_gemma":[0.0003912299,0.0002171797,0.00001837511,0.0005419186,0.00003286839,0.00000890928,0.0003558523,0.9902554,0.0002782505,0.00003945298,0.007574871,0.0002857561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000432748,0.007526271,0.9881839,0.0002097653,0.000208641,0.0005039121,0.000002148083,0.002627373,0.0003052533],"genre_scores_gemma":[0.94714,0.003505132,0.04851385,0.00001645947,0.00002316757,0.0005968016,0.00004895372,0.00009144482,0.00006418838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9467072,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05356067258545903,"score_gpt":0.2754494920241375,"score_spread":0.2218888194386785,"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."}}