{"id":"W2114555191","doi":"10.1109/icc.2009.5198947","title":"Downlink Resource Allocation for OFDMA-Based Multiservice Networks with Imperfect CSI","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quality of service; Computer network; Resource allocation; Orthogonal frequency-division multiplexing; Telecommunications link; Resource management (computing); Orthogonal frequency-division multiple access; Frequency-division multiple access; Call Admission Control; Channel state information; Channel allocation schemes; Channel (broadcasting); Wireless network; Telecommunications; Wireless","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":[0.000897649,0.000390844,0.0006441051,0.0002937361,0.0005773806,0.0009915564,0.0006507963,0.0004613809,0.0009984828],"category_scores_gemma":[0.002776091,0.0002813819,0.0001502555,0.0005855411,0.0006423732,0.0009593328,0.0008694986,0.000471068,0.000209072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293722,"about_ca_system_score_gemma":0.001076547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004155263,"about_ca_topic_score_gemma":0.003412377,"domain_scores_codex":[0.9995142,0.0001965115,0.00001633789,0.00006003433,0.0001125478,0.0001002933],"domain_scores_gemma":[0.9992377,0.0004626742,0.00009440581,0.00006362671,0.00008226246,0.00005926449],"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":[0.0001588167,0.00006078501,0.0006955083,0.00008312511,0.00002156281,0.0001591848,0.00006232989,0.9351555,0.004028417,0.02119616,0.0009414248,0.03743726],"study_design_scores_gemma":[0.000006121809,0.00001400836,0.00009237623,0.000003852914,0.000004533932,0.00001998357,0.000014899,0.9953244,0.0005726627,0.003614794,0.0003289145,0.000003515862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1037016,0.001441146,0.8873315,0.0003952571,0.00005724956,0.00005513161,0.00008960255,0.000180364,0.006748122],"genre_scores_gemma":[0.9681713,0.0003269607,0.03042137,0.00004154073,0.00003106365,0.00004371328,0.00002601149,0.00001885357,0.0009191083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004155263,"threshold_uncertainty_score":0.009386659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004106629123933301,"score_gpt":0.1969853723973859,"score_spread":0.1928787432734526,"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."}}