{"id":"W3045859422","doi":"10.1109/icc40277.2020.9149188","title":"Energy Efficient User Clustering and Hybrid Precoding for Terahertz MIMO-NOMA Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Precoding; Computer science; MIMO; Cluster analysis; Maximization; Efficient energy use; Convergence (economics); Noma; Electronic engineering; Terahertz radiation; Zero-forcing precoding; Energy consumption; Channel (broadcasting); Mathematical optimization; Telecommunications; Mathematics; Engineering; Telecommunications link; Artificial intelligence; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00004993115,0.00008832918,0.0001087227,0.00002270958,0.00004458589,0.00005975421,0.0001013515,0.00002877323,0.00001300561],"category_scores_gemma":[0.00000582039,0.00008618105,0.00003036538,0.00004867953,0.000006488823,0.00002574266,0.00005787924,0.00004419501,0.00000289962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001407293,"about_ca_system_score_gemma":0.000002738022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009578594,"about_ca_topic_score_gemma":0.000005238699,"domain_scores_codex":[0.9995499,0.00001536211,0.0001453988,0.0001100078,0.00004619884,0.0001331228],"domain_scores_gemma":[0.9996959,0.00004097024,0.00001437019,0.0001518764,0.00001375181,0.00008314392],"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":[0.00003376334,0.00002082945,0.0001380912,0.0004640704,0.0001256496,0.000005708847,0.0004543296,0.9153461,0.01836094,0.003550381,0.01228496,0.04921513],"study_design_scores_gemma":[0.0002065223,0.00001388089,0.00001212149,0.00002143645,0.000005422844,0.00000473945,0.00002095302,0.8719847,0.002142634,0.000001366411,0.1254885,0.00009775587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02425563,0.00207669,0.9685226,0.000166887,0.0001919366,0.0001785799,0.000003034371,0.0002610387,0.0043436],"genre_scores_gemma":[0.996829,0.00008644432,0.002551092,0.0002030342,0.00006901176,0.00004189274,0.000008056129,0.00002647381,0.0001849796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9725734,"threshold_uncertainty_score":0.3514361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569704506322346,"score_gpt":0.1963238967854781,"score_spread":0.1806268517222546,"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."}}