{"id":"W2981080014","doi":"10.1109/tmc.2019.2948014","title":"Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Mobile Communications Research Laboratory, Southeast University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Mathematical optimization; Computer science; Maximization; Utility maximization problem; Convexity; Optimization problem; Grid; Convex optimization; Minification; Energy consumption; Lagrangian relaxation; Regular polygon; Mathematics; Utility maximization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001194091,0.00109567,0.001112528,0.0003987789,0.0003793479,0.001096959,0.001031734,0.001067487,0.001881144],"category_scores_gemma":[0.001905284,0.0005070749,0.0006776704,0.0008343899,0.0009024802,0.001142085,0.001012202,0.0015223,0.0004239915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008552024,"about_ca_system_score_gemma":0.001139942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002843098,"about_ca_topic_score_gemma":0.003237797,"domain_scores_codex":[0.9995536,0.000186326,0.00002002018,0.0000809375,0.0001014075,0.00005767817],"domain_scores_gemma":[0.9993923,0.0003732093,0.0000701047,0.00004315632,0.00008927099,0.00003211097],"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":[0.00004578063,0.00002849619,0.0001500381,0.00004869611,0.00001917316,0.00005983204,0.00002977352,0.9690708,0.001061101,0.00865856,0.0009583444,0.01986941],"study_design_scores_gemma":[0.000006516468,0.000009201523,0.00001534928,0.000002579828,0.000002211459,0.000007119112,0.000005583562,0.9974136,0.0001517375,0.002171297,0.0002130038,0.000001766568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006600155,0.0001748616,0.9909605,0.0001445782,0.00003194607,0.00002593213,0.00003410788,0.00009600321,0.001931825],"genre_scores_gemma":[0.629921,0.0004991322,0.3634855,0.0002765776,0.00009245728,0.0002505314,0.0002418308,0.0000993698,0.005133509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002843098,"threshold_uncertainty_score":0.006315053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004130425254912459,"score_gpt":0.1818170768929881,"score_spread":0.1776866516380757,"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."}}