{"id":"W2151925996","doi":"10.1109/tvt.2008.925002","title":"Simplified Fair Scheduling and Antenna Selection Algorithms for Multiuser MIMO Orthogonal Space-Division Multiplexing Downlink","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Telecommunications link; Computer science; MIMO; Algorithm; Scheduling (production processes); Precoding; Base station; Multi-user MIMO; Multiplexing; Orthogonal frequency-division multiplexing; Greedy algorithm; Spatial multiplexing; Mathematical optimization; Channel (broadcasting); Mathematics; Computer network; 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":[0.001797435,0.0008913315,0.001007309,0.000597205,0.0005801531,0.001024093,0.001152603,0.0005997256,0.001462341],"category_scores_gemma":[0.00571898,0.000375708,0.0004213228,0.0008523088,0.0009012985,0.001220241,0.0006994595,0.0006369872,0.0003292619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905721,"about_ca_system_score_gemma":0.002146845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007103199,"about_ca_topic_score_gemma":0.007160039,"domain_scores_codex":[0.9987435,0.0005086132,0.00004292924,0.0001041045,0.0004303638,0.0001704398],"domain_scores_gemma":[0.9979674,0.001067206,0.0002228556,0.0003002324,0.0003552406,0.00008704301],"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":[0.0001104675,0.00003813864,0.000219475,0.00002708404,0.00001564473,0.00002682406,0.0000406855,0.9561757,0.001384529,0.01190302,0.0005807856,0.02947762],"study_design_scores_gemma":[0.00001819407,0.00002021158,0.00005564605,0.000001441001,0.000003911149,0.000007808228,0.000004605757,0.9961221,0.0004145014,0.003077105,0.0002710641,0.000003481761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02172555,0.0003367512,0.9759858,0.0001128546,0.00005167148,0.00006467429,0.00003946897,0.0002289881,0.001454291],"genre_scores_gemma":[0.7358142,0.0005513445,0.2607104,0.0001293162,0.0001197259,0.0001924433,0.0001243422,0.00006410776,0.002294059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007103199,"threshold_uncertainty_score":0.01412374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199948122355027,"score_gpt":0.244973631024607,"score_spread":0.2329741498010568,"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."}}