{"id":"W2885828982","doi":"10.1109/jsac.2019.2904352","title":"Spatial Deep Learning for Wireless Scheduling","year":2019,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":278,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Scheduling (production processes); Maximization; Wireless network; Wireless; Schedule; Artificial neural network; Channel (broadcasting); Distributed computing; Mathematical optimization; Computer network; Artificial intelligence; Telecommunications","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":[0.0005663761,0.0007215195,0.0005533788,0.0003642406,0.0002623006,0.0005829647,0.000886558,0.0007435356,0.002238688],"category_scores_gemma":[0.001802699,0.0003474462,0.0003459881,0.0006083241,0.0005989079,0.0009431842,0.0007896146,0.001300607,0.0003433491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001286885,"about_ca_system_score_gemma":0.001133942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007525959,"about_ca_topic_score_gemma":0.01054222,"domain_scores_codex":[0.9998152,0.00005589503,0.000008274859,0.00003628107,0.00004320523,0.00004128526],"domain_scores_gemma":[0.9994841,0.0002901601,0.00005523486,0.00004949324,0.00009485747,0.00002607289],"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":[0.00003135255,0.00003183031,0.0003119384,0.00002705712,0.00001788837,0.00001491205,0.0000121594,0.9468167,0.0006790307,0.009677461,0.001204057,0.04117556],"study_design_scores_gemma":[0.000001090934,0.000003232376,0.00001945939,0.000001089736,9.337125e-7,0.000001329001,0.000001230981,0.9970336,0.0001246861,0.002700397,0.0001123479,6.355287e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02848312,0.0008352594,0.9650186,0.000595794,0.00008053976,0.00002299849,0.0001149953,0.0006855956,0.004163095],"genre_scores_gemma":[0.8768179,0.0006868409,0.1147947,0.0002867029,0.00009187656,0.00009936698,0.000275193,0.00008425248,0.00686309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007525959,"threshold_uncertainty_score":0.01496428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666949143512008,"score_gpt":0.2678567553149181,"score_spread":0.251187263879798,"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."}}