{"id":"W2899799113","doi":"10.1109/icmcs.2018.8525955","title":"Capacity-Aware Hybrid beamforming for Multi-User Massive MIMO","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"Medical Research Council; CMC Microsystems","keywords":"Beamforming; MIMO; Base station; Telecommunications link; Computer science; Precoding; Electronic engineering; Channel capacity; Capacity optimization; Computer network; Multi-user; Channel (broadcasting); Computer engineering; Telecommunications; 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":[0.0003524956,0.0006724169,0.0004197527,0.0003108321,0.000231836,0.0003692954,0.0005423884,0.0004638711,0.001271458],"category_scores_gemma":[0.0005967857,0.0001810616,0.0003158892,0.0004278163,0.0004073044,0.0007432211,0.0006437215,0.0003694436,0.0003271402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002442976,"about_ca_system_score_gemma":0.0003614679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007677916,"about_ca_topic_score_gemma":0.001059633,"domain_scores_codex":[0.9997638,0.00005878921,0.000008051245,0.00003368005,0.00009534563,0.00004026333],"domain_scores_gemma":[0.9996902,0.000117943,0.000032116,0.00004661381,0.00008646218,0.00002672154],"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":[0.0001263949,0.00007045124,0.000735193,0.0001440853,0.00008424901,0.0001920173,0.00007249211,0.6976302,0.08320612,0.03495812,0.00229395,0.1804867],"study_design_scores_gemma":[0.00001156581,0.00009216217,0.0002041597,0.000007763628,0.00001197339,0.00009464503,0.00001021322,0.9855607,0.007156286,0.00510421,0.001725631,0.00002066064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0110849,0.0003719745,0.9857803,0.00009972089,0.0000478129,0.00001421446,0.00002553467,0.0001880536,0.002387516],"genre_scores_gemma":[0.7756805,0.0006944833,0.2202837,0.0002131105,0.0001507787,0.00009575854,0.00008663232,0.00003807036,0.002756971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001271458,"threshold_uncertainty_score":0.004253447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443850570278297,"score_gpt":0.2508809602344528,"score_spread":0.2264424545316698,"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."}}