{"id":"W2126279047","doi":"10.1109/twc.2008.060507","title":"An optimization framework for balancing throughput and fairness in wireless networks with QoS support","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Throughput; Maximum throughput scheduling; Quality of service; Fairness measure; Computer network; Max-min fairness; Wireless network; Resource allocation; Provisioning; Wireless broadband; Wireless; Radio resource management; Resource management (computing); Distributed computing; Dynamic priority scheduling; Telecommunications","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":[0.003638146,0.001721951,0.00132678,0.0009753244,0.001028498,0.001841441,0.001718277,0.00144176,0.001703135],"category_scores_gemma":[0.003200458,0.0005291223,0.0008420901,0.00144802,0.001706354,0.001824813,0.001539719,0.001648979,0.0003016887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002603918,"about_ca_system_score_gemma":0.00305902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003586541,"about_ca_topic_score_gemma":0.002824396,"domain_scores_codex":[0.9984171,0.0006356692,0.00005200785,0.0001684656,0.0005132218,0.0002135254],"domain_scores_gemma":[0.9994166,0.0003045754,0.0000847003,0.00003383977,0.0001128072,0.00004744071],"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":[0.00002570535,0.00004870101,0.00009893121,0.00006065559,0.00002263274,0.00005309772,0.00004311582,0.8200381,0.001396002,0.1638223,0.001371109,0.01301963],"study_design_scores_gemma":[0.00001219158,0.00002429691,0.00005753247,0.00001128751,0.00001041098,0.00002033801,0.00001187708,0.9637387,0.0002955239,0.03461571,0.001191029,0.00001106187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003549528,0.0003716837,0.9919336,0.0002012536,0.00004472086,0.00004570168,0.00002872896,0.00004893494,0.003775731],"genre_scores_gemma":[0.5379539,0.002321041,0.4497591,0.0003375666,0.000410683,0.0005819558,0.0001432191,0.0001494357,0.00834327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003638146,"threshold_uncertainty_score":0.01924062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685526415569001,"score_gpt":0.2525164520931312,"score_spread":0.2356611879374412,"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."}}