{"id":"W2066218456","doi":"10.1109/iswpc.2008.4556217","title":"Fair scheduling in multirate wireless access networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Maximum throughput scheduling; Scheduling (production processes); Link adaptation; Wireless network; Physical layer; Wireless distribution system; Proportionally fair; Media access control; Wireless; Access control; Provisioning; Link layer; Access network; Round-robin scheduling; Distributed computing; Quality of service; Dynamic priority scheduling; Channel (broadcasting); Wi-Fi; Fading; 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":[0.006467306,0.0007088589,0.0008867398,0.0008867115,0.001202418,0.001910512,0.00138427,0.0009568978,0.001255524],"category_scores_gemma":[0.01534568,0.000448153,0.0004048202,0.001022668,0.002140067,0.002682761,0.001047312,0.0009771269,0.0001977265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002150183,"about_ca_system_score_gemma":0.001940363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00453431,"about_ca_topic_score_gemma":0.002783223,"domain_scores_codex":[0.9971356,0.001479191,0.0000936154,0.00022132,0.0008091635,0.0002610442],"domain_scores_gemma":[0.9950459,0.003618382,0.0003369912,0.0004199767,0.0004105399,0.0001682649],"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":[0.0001327827,0.00006023478,0.0003508359,0.00008345148,0.0000254344,0.00007018184,0.00009986424,0.7137127,0.001749458,0.2505581,0.000899236,0.03225775],"study_design_scores_gemma":[0.00003548307,0.00003776112,0.0001035413,0.00001449591,0.000009747344,0.00002368396,0.00001686257,0.9108764,0.0007729043,0.08615906,0.001936023,0.00001405313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02064913,0.002232312,0.9724291,0.0003143778,0.0001940364,0.0001079238,0.00003398935,0.0001734696,0.003865791],"genre_scores_gemma":[0.7684269,0.002269939,0.2256601,0.0001401207,0.000266456,0.0002150561,0.00004728504,0.00007629704,0.002897838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006467306,"threshold_uncertainty_score":0.03420281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778363761885003,"score_gpt":0.2336172245206478,"score_spread":0.2158335869017977,"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."}}