{"id":"W4312370603","doi":"10.1109/access.2022.3224197","title":"SLINR-Based Downlink Optimization in MU-MIMO Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommunications link; Computer science; Beamforming; Maximization; MIMO; Channel state information; Overhead (engineering); Mathematical optimization; Algorithm; Theoretical computer science; Mathematics; Wireless; Telecommunications","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":[0.001514926,0.001098753,0.001314257,0.000341788,0.000399623,0.001024746,0.0009542797,0.0007736856,0.0008205069],"category_scores_gemma":[0.002081297,0.0005093094,0.0003841805,0.0006393023,0.0008042441,0.0009405566,0.0009573596,0.0007297901,0.0002791252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170372,"about_ca_system_score_gemma":0.001326403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002781854,"about_ca_topic_score_gemma":0.002936483,"domain_scores_codex":[0.9992163,0.0003709166,0.00002100141,0.0001184317,0.0001619152,0.0001115008],"domain_scores_gemma":[0.9993196,0.0003781267,0.0001056811,0.00004295462,0.0001087643,0.00004499133],"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":[0.00003773689,0.00001551295,0.0001331553,0.00002697536,0.00001391915,0.00002495006,0.00001720291,0.985346,0.001160288,0.004174342,0.000359269,0.00869075],"study_design_scores_gemma":[0.000003984708,0.00002374295,0.0000458514,0.000002516947,0.000002663801,0.000007789458,0.000005556569,0.9977138,0.0003648371,0.001712308,0.0001142355,0.000002687617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02868859,0.0007169538,0.964761,0.0002953494,0.0000374544,0.0000356035,0.00006656104,0.0002982586,0.005100196],"genre_scores_gemma":[0.8855284,0.0007363925,0.1094887,0.0002018287,0.00008401205,0.0001157394,0.00009503013,0.00008217367,0.003667615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002781854,"threshold_uncertainty_score":0.008491635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389626892102377,"score_gpt":0.2503536935684686,"score_spread":0.2364574246474448,"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."}}