{"id":"W2063783961","doi":"10.1109/twc.2014.2367032","title":"Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Macrocell; Computer science; Cognitive radio; Efficient energy use; Mathematical optimization; Telecommunications link; Spectral efficiency; Convergence (economics); Stochastic geometry; Wireless; Interference (communication); Maximization; Transmitter power output; Optimization problem; Computer network; Algorithm; Telecommunications; Base station; Mathematics; Channel (broadcasting); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001836661,0.0002449958,0.0002588325,0.0003462218,0.0002983388,0.00004902874,0.0004934469,0.0002096485,0.00004654739],"category_scores_gemma":[0.00001880457,0.0002895128,0.00007605921,0.001025305,0.0001196804,0.0002657548,0.000005100789,0.0005888167,0.00003183103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710276,"about_ca_system_score_gemma":0.00001955086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006239131,"about_ca_topic_score_gemma":0.0004240441,"domain_scores_codex":[0.9985957,0.0002068928,0.0004987233,0.0002502049,0.000138719,0.0003097516],"domain_scores_gemma":[0.9978814,0.0006826911,0.00008373577,0.001105468,0.0001581875,0.00008848299],"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":[0.000009344185,0.0001395646,0.00002074836,0.000008856371,0.00002084288,2.606148e-7,0.0003074109,0.9457121,0.00007683395,0.00231523,0.00001588918,0.05137288],"study_design_scores_gemma":[0.0005536861,0.00002969612,0.00002222393,0.0002419681,0.0000266168,0.000002980068,0.00008702007,0.9972122,0.000929381,0.0003148179,0.0002877463,0.000291704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008144418,0.0002437937,0.9946309,0.0001110864,0.0004519933,0.0002746577,0.00002216811,0.0004838139,0.00296718],"genre_scores_gemma":[0.9859837,0.0009550859,0.01230496,0.0001278269,0.00004464542,0.0003386732,0.0000719384,0.000074304,0.00009887516],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9851692,"threshold_uncertainty_score":0.9999557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369440973927892,"score_gpt":0.2516235502351504,"score_spread":0.2379291404958715,"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."}}