{"id":"W4382119870","doi":"10.1109/lsp.2023.3289438","title":"Global Optimization of Long-Term Average Proportional Fair Throughput via Convex Reformulation","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Toronto","funders":"","keywords":"Maximization; Term (time); Mathematics; Regular polygon; Mathematical optimization; Convex function; Convex optimization; Proportionally fair; Combinatorics; Throughput; Optimization problem; Discrete mathematics; Algorithm; Applied mathematics; Computer science","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":[0.0001140197,0.0002073058,0.0002150592,0.0001180036,0.0001104005,0.00004091907,0.0001311132,0.00010389,0.00003301858],"category_scores_gemma":[0.000005637316,0.0002197943,0.00005584941,0.0008771899,0.00007670803,0.0006822533,0.00001765907,0.0001116202,0.0000143383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054205,"about_ca_system_score_gemma":0.00003302475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002102975,"about_ca_topic_score_gemma":0.000001630984,"domain_scores_codex":[0.9986226,0.00002106583,0.0004298666,0.0002489244,0.0003716949,0.0003058341],"domain_scores_gemma":[0.9995131,0.00002045995,0.0001718146,0.0001283116,0.0001124784,0.00005386531],"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":[0.00001927891,0.00001225639,0.002810762,0.0002329753,0.00002247537,0.00001306939,0.00008858959,0.9720532,0.00848408,0.000008485444,0.0002335039,0.01602137],"study_design_scores_gemma":[0.0003181301,0.00001680112,0.004934897,0.000139289,0.00001644594,0.00001029889,0.000004785304,0.9907005,0.003528672,0.00009191506,0.000005727607,0.0002324714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1257532,0.00005285685,0.8728514,0.0001068752,0.0002351505,0.0002113416,0.000009202403,0.0006576311,0.0001222764],"genre_scores_gemma":[0.9888201,0.00002250699,0.01046639,0.000114603,0.0002216888,0.00002317419,0.0002654093,0.00005221452,0.00001395838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8630669,"threshold_uncertainty_score":0.896295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107198564170223,"score_gpt":0.2378393875735708,"score_spread":0.2271195311565485,"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."}}