{"id":"W2182378989","doi":"10.1109/pimrc.2015.7343450","title":"Min-max energy-efficiency analysis of multiuser wireless systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Fractional programming; Computer science; Iterative method; Energy (signal processing); Power (physics); Efficient energy use; Convex optimization; Wireless; Wireless network; Parametric programming; Nonlinear programming; Parametric statistics; Multiuser detection; Linear programming; Geometric programming; Algorithm; Nonlinear system; Regular polygon; Mathematics; Computer network; Code division multiple access; Telecommunications; Engineering; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008638466,0.000122895,0.0002969159,0.0002579576,0.00001479789,0.00001284615,0.0001255394,0.00007057512,0.00001911183],"category_scores_gemma":[0.00001144685,0.0001149812,0.00006506848,0.001302679,0.00002239224,0.000119488,0.0000196694,0.00004164177,0.00000566745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006771088,"about_ca_system_score_gemma":0.00001079801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009232812,"about_ca_topic_score_gemma":0.00005860645,"domain_scores_codex":[0.9991672,0.00001974995,0.000291058,0.0001396282,0.000200938,0.0001814364],"domain_scores_gemma":[0.9994233,0.0000435737,0.00005137402,0.0002613652,0.0001282184,0.00009218504],"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":[0.00000276171,0.00002153984,0.0005261718,0.00001231112,0.0001794354,0.000001121528,0.0001192854,0.9954949,0.0002453514,0.001858008,0.0004037147,0.0011354],"study_design_scores_gemma":[0.0001636021,0.00001208757,0.00005901058,0.000009992297,0.0001331548,3.545859e-7,0.0001402799,0.9978123,0.0008913639,0.00000775363,0.0006355179,0.0001345478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05893292,0.0004770149,0.9326593,0.0000033921,0.0002870078,0.0000610377,0.000005648857,0.0002688789,0.007304811],"genre_scores_gemma":[0.9960878,0.00005385898,0.003217563,0.000005356943,0.00003899648,0.00001303669,0.00003147893,0.00002651416,0.0005253935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9371549,"threshold_uncertainty_score":0.4688798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248457515204274,"score_gpt":0.2120065672642627,"score_spread":0.19952199211222,"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."}}