{"id":"W2916154478","doi":"10.1109/glocom.2018.8647825","title":"Energy Efficient Power and Flow Control in Millimeter Wave Backhaul Heterogeneous Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Backhaul (telecommunications); Quasiconvex function; Computer science; Power control; Efficient energy use; Computer network; Benchmark (surveying); Throughput; Heterogeneous network; Power flow; Mathematical optimization; Distributed computing; Regular polygon; Power (physics); Convex optimization; Wireless; Base station; Wireless network; Engineering; Electric power system; Telecommunications; Mathematics; Convex combination; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003214,0.0007564285,0.0006340088,0.0003009054,0.0003720754,0.0009378149,0.0005257083,0.0005526459,0.0006386827],"category_scores_gemma":[0.001854649,0.0002435547,0.0002802302,0.0005224255,0.0008229265,0.001052745,0.0007253761,0.000470513,0.00005990689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009543574,"about_ca_system_score_gemma":0.0005031317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00362235,"about_ca_topic_score_gemma":0.002099232,"domain_scores_codex":[0.9996336,0.000149701,0.00001014585,0.00006259496,0.00006755373,0.00007640241],"domain_scores_gemma":[0.9992119,0.0005309482,0.000136782,0.00003659078,0.00005760992,0.00002609704],"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":[0.00003207231,0.00002080761,0.0002034015,0.00001335451,0.00001076236,0.00002694269,0.00001345276,0.9866673,0.001560048,0.004437319,0.0001204088,0.006894139],"study_design_scores_gemma":[0.000006077927,0.00002549327,0.00009949697,0.00000163663,0.000003987313,0.000005967353,0.00001083431,0.997225,0.0005401161,0.001947158,0.0001316348,0.000002551776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09516311,0.0005386151,0.900355,0.0002887512,0.00003632631,0.00003543679,0.00003690791,0.0000815129,0.003464333],"genre_scores_gemma":[0.9769216,0.0002821849,0.02159966,0.000053605,0.00002251655,0.0000322572,0.00001592824,0.00001498369,0.001057334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00362235,"threshold_uncertainty_score":0.007202566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008371120568106731,"score_gpt":0.1856586880910611,"score_spread":0.1772875675229543,"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."}}