{"id":"W4288640804","doi":"10.48550/arxiv.1901.02111","title":"Scheduling for VoLTE: Resource Allocation Optimization and\\n Low-Complexity Algorithms","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Scheduling (production processes); Dynamic priority scheduling; Fair-share scheduling; Mathematical optimization; Proportionally fair; Maximization; Rate-monotonic scheduling; Round-robin scheduling; Quality of service; Optimization problem; Algorithm; Computer network; 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":[0.001240252,0.0007153585,0.0009573177,0.0004950438,0.0006384334,0.001533718,0.001037594,0.001256933,0.004172469],"category_scores_gemma":[0.003564463,0.0003987812,0.0005356077,0.0007427407,0.0008030859,0.001290748,0.00106452,0.001518852,0.0005037868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001704,"about_ca_system_score_gemma":0.001779297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005242016,"about_ca_topic_score_gemma":0.006104131,"domain_scores_codex":[0.9993703,0.0002323942,0.00002220895,0.0001139537,0.0001448413,0.0001163558],"domain_scores_gemma":[0.9990355,0.0007027164,0.00007011771,0.00006926308,0.00007597129,0.00004649482],"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":[0.00006287639,0.00006498606,0.00026758,0.00004915211,0.00001778081,0.00003129141,0.00004010641,0.9227156,0.0006325292,0.03733668,0.002139698,0.03664175],"study_design_scores_gemma":[0.000009505783,0.000007749868,0.00002434978,0.000002714747,0.000001593,0.000005458358,0.000005790489,0.9898004,0.0001093757,0.009586522,0.0004448438,0.000001701951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01146881,0.0004651653,0.9797356,0.000698184,0.00007963901,0.00006442417,0.00006613132,0.0003495422,0.007072505],"genre_scores_gemma":[0.4515347,0.0007198148,0.5384667,0.0005213369,0.0001888596,0.0002755074,0.0002335468,0.0001735033,0.007885999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005242016,"threshold_uncertainty_score":0.01395833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05259215605244581,"score_gpt":0.1897813181706216,"score_spread":0.1371891621181758,"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."}}