{"id":"W2408026441","doi":"10.1109/icassp.2016.7472334","title":"Coordinated uplink scheduling and beamforming for wireless cellular networks via sum-of-ratio programming and matching","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Telecommunications link; Computer science; Beamforming; Scheduling (production processes); Cellular network; Wireless network; Quadratic programming; Mathematical optimization; Optimization problem; Wireless; Computer network; Algorithm; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001712492,0.0001425901,0.000203799,0.00006188411,0.00008037583,0.0000295899,0.00004469679,0.00009593288,0.000002016722],"category_scores_gemma":[0.00001285253,0.0001141767,0.00002141834,0.00009162777,0.00002867232,0.0003292602,0.0000273332,0.00005318761,2.794576e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002870665,"about_ca_system_score_gemma":0.000004576897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001022323,"about_ca_topic_score_gemma":0.000007841243,"domain_scores_codex":[0.9992667,0.00000700386,0.0002880488,0.0001674832,0.00004147785,0.0002293038],"domain_scores_gemma":[0.9995971,0.0001168389,0.00006487795,0.0001008257,0.00006236107,0.00005804866],"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":[0.00001609989,0.00000859514,0.0007876604,0.0004596411,0.00005828115,0.000001039742,0.0003507509,0.6315687,0.1770925,0.001928213,0.000003380901,0.1877251],"study_design_scores_gemma":[0.0005081452,0.00002443996,0.000005836919,0.0002234156,0.00001635835,0.000004861318,0.0001668172,0.9735468,0.02510291,0.0001811604,0.00003885693,0.0001803786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08017358,0.0004574155,0.9185588,0.0000125917,0.0001107403,0.0004376047,0.000001165878,0.0002079735,0.00004006764],"genre_scores_gemma":[0.8101746,0.00004378463,0.1896053,0.000002212413,0.00004831659,0.00003674606,0.000006158325,0.00004015068,0.00004275583],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.730001,"threshold_uncertainty_score":0.4655989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006491558872983382,"score_gpt":0.2069701156365205,"score_spread":0.2004785567635372,"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."}}