{"id":"W2107601408","doi":"10.1109/icc.2004.1312633","title":"Optimal and suboptimal scheduling over time varying flat fading channels","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fading; Markov decision process; Scheduling (production processes); Mathematical optimization; Computer science; Markov process; Minification; Dynamic programming; Channel (broadcasting); Algorithm; Mathematics; Telecommunications; Statistics","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":[0.00006446598,0.0001892018,0.000170695,0.00009274944,0.00009552995,0.00006307232,0.00007022692,0.0000929701,0.0001106104],"category_scores_gemma":[0.00001020621,0.0002048993,0.00003093246,0.0001943081,0.00002605323,0.0005055728,0.00004591382,0.000151537,0.00005081324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008960515,"about_ca_system_score_gemma":0.000007688921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003267051,"about_ca_topic_score_gemma":4.462556e-7,"domain_scores_codex":[0.9991461,0.000005290567,0.0001830805,0.0002113448,0.0001112641,0.0003429155],"domain_scores_gemma":[0.9997098,0.00003054315,0.0000225265,0.000122384,0.00002042043,0.00009434484],"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":[0.000005233286,0.000003249219,0.00004390742,0.00001627258,0.0000175692,0.00000628679,0.0001532833,0.9903635,0.008272046,0.0003638843,0.0000128346,0.000741933],"study_design_scores_gemma":[0.0005455172,0.00001583979,0.00004055638,0.00005688634,0.000009385823,0.00001618019,0.00001110461,0.989789,0.009066063,0.0001424421,0.00004572052,0.0002613088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4250354,0.0002625809,0.5721171,0.0000198923,0.0001379984,0.00009785428,8.722278e-7,0.0005265734,0.001801731],"genre_scores_gemma":[0.7822382,0.00008980409,0.2173099,0.0000267638,0.0001614215,0.000007207509,0.000009448341,0.00005527743,0.0001019689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3572028,"threshold_uncertainty_score":0.8355551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006876094702822668,"score_gpt":0.207351388169092,"score_spread":0.2004752934662693,"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."}}