{"id":"W4285118711","doi":"10.1109/tvt.2022.3177132","title":"Energy-Efficient Design for IRS-Empowered Uplink MIMO-NOMA Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Telecommunications link; Beamforming; MIMO; Base station; Benchmark (surveying); Computer science; Optimization problem; Wireless; Noma; Iterative method; Mathematical optimization; Efficient energy use; Convex optimization; Convergence (economics); Multi-user MIMO; Electronic engineering; Engineering; Computer network; Algorithm; Telecommunications; Regular polygon; Mathematics; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005183675,0.0008675349,0.0006534231,0.0002382319,0.0003602465,0.0009882997,0.0007208264,0.0005818313,0.00224909],"category_scores_gemma":[0.0009625083,0.000392539,0.0003784982,0.0004307531,0.0005099302,0.0005247419,0.0008083331,0.000632002,0.0006113878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004729547,"about_ca_system_score_gemma":0.0006854509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050231,"about_ca_topic_score_gemma":0.001951832,"domain_scores_codex":[0.999455,0.0002127754,0.00001929194,0.00006551666,0.0001659934,0.00008145699],"domain_scores_gemma":[0.9996722,0.0001244829,0.00006200525,0.00003064744,0.0000901704,0.00002053024],"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":[0.0001151292,0.00006643153,0.0005444863,0.0001543251,0.00005798805,0.0001767395,0.0001430403,0.9000522,0.02419013,0.02480745,0.001555492,0.04813656],"study_design_scores_gemma":[0.000007918623,0.00007319832,0.0001062088,0.000007710662,0.000009451626,0.00003515974,0.00002220623,0.9955989,0.00154694,0.001702414,0.0008828662,0.000007050227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02514133,0.0004889392,0.9614803,0.0001442766,0.00005664878,0.00005542117,0.00004586753,0.0001727721,0.01241439],"genre_scores_gemma":[0.9072443,0.0005348293,0.08822866,0.0001020379,0.00005764617,0.0001617532,0.00005603566,0.00003305776,0.003581637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00224909,"threshold_uncertainty_score":0.007523954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744681865773377,"score_gpt":0.2254425193935705,"score_spread":0.2079957007358367,"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."}}