{"id":"W2075146768","doi":"10.1109/tvt.2011.2162428","title":"Net Throughput Maximization of Per-Chunk User Scheduling for MIMO-OFDM Downlink","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Telecommunications link; Scheduling (production processes); Orthogonal frequency-division multiplexing; Computer science; MIMO; Throughput; Multiplexing; Real-time computing; Channel state information; Overhead (engineering); Computer network; Channel (broadcasting); Mathematics; Wireless; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007323985,0.0002381866,0.0003003269,0.0004154635,0.0001139924,0.000007185885,0.000222194,0.0004834015,0.00006774566],"category_scores_gemma":[0.00000794583,0.0002654963,0.0001200356,0.0005724985,0.0001207023,0.0001962021,0.000001921286,0.0003238658,0.00001804698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007932434,"about_ca_system_score_gemma":0.00001739885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005435193,"about_ca_topic_score_gemma":0.00001962742,"domain_scores_codex":[0.9988533,0.00001610493,0.0003881142,0.0002971712,0.0001156309,0.0003296657],"domain_scores_gemma":[0.9992415,0.00003856345,0.00008503108,0.0004488497,0.0001448817,0.0000411618],"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":[0.00003358471,0.00008338765,0.00002356187,0.00005867603,0.00008723411,0.000001667035,0.0001172582,0.9753746,0.008417712,0.001069013,0.00001823828,0.01471508],"study_design_scores_gemma":[0.0008394817,0.0001864559,0.00002096269,0.00007484326,0.0001012585,0.00001544421,0.0001189936,0.6789043,0.316851,0.001600691,0.0009473743,0.0003391984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04109898,0.0002357725,0.9564791,0.00007899938,0.0004902782,0.0005093045,0.00002627329,0.0009547374,0.0001265023],"genre_scores_gemma":[0.7700353,0.0003119136,0.2293048,0.00002040966,0.00002719776,0.0001724609,0.00001531116,0.00007543696,0.00003711573],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7289364,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460372543897682,"score_gpt":0.2156156997942234,"score_spread":0.2010119743552466,"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."}}