{"id":"W4388919309","doi":"10.1109/lwc.2023.3335622","title":"Robust Design for IRS-Assisted MISO-NOMA Systems: A DRL-Based Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; HORIZON EUROPE Marie Sklodowska-Curie Actions; Natural Sciences and Engineering Research Council of Canada","keywords":"Noma; Computer science; Computer network; Telecommunications link","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001177166,0.001255282,0.001179419,0.0004029916,0.0003213164,0.001068302,0.0009475012,0.001041233,0.001798948],"category_scores_gemma":[0.002282681,0.0005770409,0.0006506476,0.000343381,0.001090573,0.0008313563,0.001247102,0.001181915,0.0005649076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007315972,"about_ca_system_score_gemma":0.0009262648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850704,"about_ca_topic_score_gemma":0.001537184,"domain_scores_codex":[0.9992523,0.0002287603,0.00003092951,0.0001688973,0.0002263273,0.00009279457],"domain_scores_gemma":[0.9990647,0.0004024002,0.0002141323,0.00006360211,0.0002171607,0.00003803452],"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":[0.00003702584,0.00001874708,0.0001876279,0.0000604109,0.00003369737,0.000052568,0.00005024812,0.970124,0.003009134,0.009170664,0.0003699704,0.01688592],"study_design_scores_gemma":[0.000005010383,0.00003059517,0.00002521214,0.000004319792,0.00000513407,0.000009007042,0.000005017555,0.9981529,0.000386924,0.00104092,0.0003310515,0.000003896527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004125535,0.0001796871,0.9929649,0.0001186223,0.00002458866,0.00002297938,0.00001584139,0.0001102092,0.002437637],"genre_scores_gemma":[0.852559,0.0004132462,0.141928,0.0002373922,0.00009245246,0.0002234374,0.00007828759,0.00007808641,0.004390081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001850704,"threshold_uncertainty_score":0.006225526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1182114896886735,"score_gpt":0.2700910521237607,"score_spread":0.1518795624350872,"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."}}