{"id":"W2148797190","doi":"10.1002/wcm.2635","title":"Energy Efficiency Architecture Design for Heterogeneous Cellular Networks","year":2015,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Efficient energy use; Macrocell; Heterogeneous network; Spectral efficiency; Standardization; Metric (unit); Architecture; Distributed computing; Cognitive radio; Computer network; Cognitive network; Network architecture; Computer architecture; Telecommunications; Wireless; Wireless network; Base station","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":[0.0001932276,0.0001333542,0.0001627931,0.00005738233,0.0002062218,0.00004140467,0.0003134522,0.00007010738,3.770221e-7],"category_scores_gemma":[0.000007918991,0.0001412105,0.00003156871,0.0001413471,0.00005268829,0.00004190579,0.0001387243,0.00009929705,4.795363e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004448301,"about_ca_system_score_gemma":0.0000142247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000897677,"about_ca_topic_score_gemma":0.000005829559,"domain_scores_codex":[0.9993033,0.0000691572,0.0002335576,0.0001417414,0.00004803687,0.0002042421],"domain_scores_gemma":[0.9989442,0.000220313,0.00005554102,0.0006058282,0.00008630612,0.00008786499],"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":[0.000002234535,0.00001436213,0.000006615925,0.0000140317,0.00001039049,2.562061e-7,0.0003732392,0.9285366,0.0003970505,0.000331853,0.00003657311,0.0702768],"study_design_scores_gemma":[0.0002467712,0.00005110592,3.508273e-7,0.00003860943,0.000008792003,0.00001137846,0.00009456259,0.9942286,0.0006362501,0.000111834,0.004420591,0.0001511593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005682766,0.009032256,0.9843829,0.00001030245,0.000131978,0.000355568,0.000002771676,0.0002628858,0.000138568],"genre_scores_gemma":[0.912987,0.0002730993,0.08645132,0.00001413634,0.00006103282,0.0001231836,0.00003871715,0.00003834588,0.00001314847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9073042,"threshold_uncertainty_score":0.5758398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162142314824988,"score_gpt":0.2374257400543942,"score_spread":0.2158043169061443,"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."}}