{"id":"W2028067114","doi":"10.1109/icc.2008.70","title":"A New Modeling Approach for Utility-Based Resource Allocation in OFDM Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Orthogonal frequency-division multiplexing; Resource allocation; Computer science; Heuristic; Nonlinear programming; Integer programming; Genetic algorithm; Optimization problem; Integer (computer science); Convergence (economics); Base station; Nonlinear system; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008395899,0.001000933,0.0007043747,0.0005152318,0.0004585716,0.001317838,0.001836543,0.001132361,0.001992401],"category_scores_gemma":[0.001436754,0.0004007828,0.000806349,0.0009468113,0.0006705383,0.002051468,0.0007950159,0.00163179,0.0003937803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348342,"about_ca_system_score_gemma":0.001033214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0035746,"about_ca_topic_score_gemma":0.003011517,"domain_scores_codex":[0.9995561,0.0001616018,0.00001852558,0.00006311001,0.0001514485,0.00004928145],"domain_scores_gemma":[0.9996953,0.0001565842,0.00004123149,0.00002006706,0.00006808843,0.00001882898],"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":[0.00001173088,0.00003230766,0.0001963046,0.00005125724,0.00002052485,0.000105604,0.00006786894,0.8071237,0.001313238,0.177062,0.001299474,0.01271605],"study_design_scores_gemma":[0.000002295661,0.00000833297,0.00002654592,0.00000526266,0.000004603119,0.00002014281,0.000007535932,0.9845434,0.0001296386,0.0137677,0.001479457,0.000005085935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001869094,0.0002427235,0.993821,0.0002849258,0.00004735318,0.00002406836,0.00004233994,0.00004252212,0.003626073],"genre_scores_gemma":[0.6158914,0.00384251,0.3576117,0.0004355911,0.0004851989,0.0007819601,0.0002460222,0.0001398527,0.02056583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0035746,"threshold_uncertainty_score":0.00978297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371426467168177,"score_gpt":0.2139509642087093,"score_spread":0.1902366995370275,"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."}}