{"id":"W2152831210","doi":"10.1109/wcnc.2011.5779129","title":"Queue-aware adaptive resource allocation for OFDMA systems supporting mixed services","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Queue; Resource allocation; Distributed computing; Upper and lower bounds; Queueing theory; Set (abstract data type); Resource management (computing); Throughput; Mathematical optimization; Computer network; Wireless; Telecommunications; Mathematics","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":[0.001322727,0.0006455893,0.0006283105,0.0004026431,0.0004763644,0.001001365,0.001097346,0.0005986576,0.00103535],"category_scores_gemma":[0.003290822,0.0003200175,0.0002977384,0.0006971169,0.0005446681,0.001100044,0.0008664486,0.0007789882,0.0001607017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007878203,"about_ca_system_score_gemma":0.001164864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002883413,"about_ca_topic_score_gemma":0.003569707,"domain_scores_codex":[0.9994894,0.0001988436,0.00002278529,0.00006781004,0.0001337209,0.00008749584],"domain_scores_gemma":[0.9989994,0.0006921512,0.00009638061,0.00004087844,0.0001216701,0.0000494648],"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":[0.0002133834,0.00009343274,0.0005703882,0.0001143097,0.00004741357,0.0000710546,0.00008904564,0.9033473,0.004969412,0.02069104,0.001315482,0.06847768],"study_design_scores_gemma":[0.00001049889,0.00001970665,0.00004837941,0.000002218447,0.000004318908,0.000009897181,0.000007435387,0.9970315,0.000272459,0.002426308,0.0001643024,0.000003066784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02086423,0.0004480257,0.9774251,0.0001170824,0.00003803235,0.00003319412,0.00002026494,0.00008351736,0.0009704916],"genre_scores_gemma":[0.8346446,0.0004408478,0.1632332,0.00008844976,0.00008340946,0.00009160204,0.00003316916,0.00003537901,0.001349366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002883413,"threshold_uncertainty_score":0.00699538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949663727156808,"score_gpt":0.2122415247119238,"score_spread":0.1927448874403557,"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."}}