{"id":"W2966589507","doi":"10.22215/etd/2013-10385","title":"Towards Efficient and Fair Radio Resource Allocation Schemes for Interference-Limited Celluar Networks","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Toronto Metropolitan University","funders":"","keywords":"Subgradient method; Mathematical optimization; Resource allocation; Computer science; Optimization problem; Maximization; Interference (communication); Convex optimization; Max-min fairness; Cellular network; Mathematics; Regular polygon; Computer network","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":[0.00008658616,0.0003994423,0.0003573014,0.0001735551,0.00008902807,0.00009385597,0.0001664869,0.0004480733,0.00005260335],"category_scores_gemma":[0.00003160616,0.0004056499,0.00007099385,0.0002215809,0.00002244518,0.0001054845,0.00001803834,0.0002757994,0.000006732985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205624,"about_ca_system_score_gemma":0.00002095465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008428892,"about_ca_topic_score_gemma":0.00002700905,"domain_scores_codex":[0.9986919,0.00001882657,0.0004115458,0.0003885165,0.0001442109,0.0003450231],"domain_scores_gemma":[0.9992648,0.00008750181,0.0001189581,0.0002616547,0.0001697405,0.00009728911],"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":[0.00003497113,0.00001221835,0.000005934779,0.0002427945,0.00005423557,2.044506e-7,0.000283009,0.9423757,0.0002289858,0.0007028661,0.003787857,0.05227126],"study_design_scores_gemma":[0.0003234613,0.00003776094,0.0001028222,0.0002190181,0.00005246001,8.986099e-7,0.0003955506,0.990681,0.002604204,0.00004211863,0.005105916,0.0004347737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03274288,0.002141202,0.9580312,0.00001897064,0.0007242125,0.001151468,0.000008395094,0.0006328888,0.004548756],"genre_scores_gemma":[0.9570159,0.001063458,0.02830798,0.00003223321,0.0004169036,0.0006204688,0.006606369,0.0002466141,0.00569007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9297233,"threshold_uncertainty_score":0.9998395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0073414992046564,"score_gpt":0.2156044238632296,"score_spread":0.2082629246585732,"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."}}