{"id":"W4285502908","doi":"10.1109/cwit55308.2022.9817672","title":"A Hybrid Random-Greedy Approach to User Selection for MU-MIMO Based on Pairwise Metrics","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Metric (unit); Selection (genetic algorithm); Greedy algorithm; Pairwise comparison; Computer science; MIMO; Subspace topology; Telecommunications link; Linear subspace; Mathematical optimization; Algorithm; Performance metric; Monte Carlo method; Mathematics; Statistics; Artificial intelligence; Engineering; Beamforming; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001418525,0.001442757,0.001176091,0.0009659879,0.0005687003,0.0007054926,0.001674128,0.0005159552,0.001196551],"category_scores_gemma":[0.002930681,0.0003711736,0.0005908433,0.001130602,0.0007017135,0.001202403,0.001401,0.0005502517,0.0004459662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006121464,"about_ca_system_score_gemma":0.000900596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007291287,"about_ca_topic_score_gemma":0.001320695,"domain_scores_codex":[0.9980282,0.0009854308,0.00006647811,0.0002029375,0.0006021908,0.0001146641],"domain_scores_gemma":[0.998952,0.0004670995,0.0001202748,0.0001597642,0.0002165491,0.00008436529],"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":[0.0002721153,0.0001452508,0.001520118,0.000230911,0.0002043081,0.0001792197,0.0001594267,0.6405293,0.02472403,0.06425411,0.003370475,0.2644107],"study_design_scores_gemma":[0.00002414826,0.0003557541,0.0003290232,0.00001017265,0.00002521466,0.0001850164,0.00002361095,0.98107,0.004031405,0.01165616,0.002258883,0.00003063494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005656097,0.0002389758,0.9927598,0.00004860829,0.00002464666,0.00005427235,0.00001422623,0.0001325785,0.001070833],"genre_scores_gemma":[0.4835587,0.0005045854,0.5125601,0.0001365659,0.0001310169,0.0003163792,0.0001085854,0.0001025211,0.002581561],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001674128,"threshold_uncertainty_score":0.007502019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367578010119688,"score_gpt":0.2133034367869856,"score_spread":0.1996276566857887,"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."}}