{"id":"W3000366215","doi":"10.1109/ojcoms.2019.2953576","title":"Energy Efficient User Association, Power, and Flow Control in Millimeter Wave Backhaul Heterogeneous Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Power control; Backhaul (telecommunications); Multi-commodity flow problem; Flow network; Computational complexity theory; Integer programming; Convex optimization; Subgradient method; Heuristic; Algorithm; Power (physics); Mathematics; Regular polygon; Base station; Computer network","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":[0.0008284813,0.0007116246,0.0007549162,0.0004012119,0.0005290045,0.00107838,0.0006015919,0.0004148975,0.0005819391],"category_scores_gemma":[0.001361375,0.0002441392,0.0003008681,0.001087068,0.0006042925,0.001112576,0.0008278207,0.0005579588,0.00009114784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054329,"about_ca_system_score_gemma":0.0007268542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005437926,"about_ca_topic_score_gemma":0.003969284,"domain_scores_codex":[0.999465,0.0002075643,0.00001652994,0.00008008967,0.0001231262,0.0001077197],"domain_scores_gemma":[0.9995462,0.0002698074,0.00008460738,0.00003256776,0.00004638074,0.00002040158],"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":[0.00005796811,0.0000375662,0.0003578207,0.00002815925,0.00001410682,0.00005804888,0.0000317303,0.9556472,0.002952912,0.0082218,0.0003842266,0.03220845],"study_design_scores_gemma":[0.000005055045,0.00001718966,0.00008322396,0.000001683392,0.000003525475,0.000008548783,0.0000153598,0.9968494,0.0007341407,0.002060309,0.0002191219,0.00000246031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05714029,0.0004813727,0.9393823,0.0001870912,0.00002146003,0.00003517379,0.00003053563,0.0001296477,0.002592121],"genre_scores_gemma":[0.9415227,0.0005667164,0.05610341,0.0000622958,0.00003312091,0.00005081982,0.00003868218,0.00002260822,0.001599692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005437926,"threshold_uncertainty_score":0.01081258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581687145362152,"score_gpt":0.2263531560453562,"score_spread":0.2105362845917347,"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."}}