{"id":"W2567213782","doi":"10.1155/2016/7172515","title":"Power Allocation Scheme for Femto-to-Macro Downlink Interference Reduction for Smart Devices in Ambient Intelligence","year":2016,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; National Research Foundation of Korea; Government of Jiangsu Province; National Research Foundation","keywords":"Femtocell; Macrocell; Femto-; Computer science; Throughput; Interference (communication); Telecommunications link; Reduction (mathematics); Computer network; Macro; LTE Advanced; Wireless; Telecommunications; Channel (broadcasting); 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":[0.0004668322,0.00019227,0.0002300975,0.0003493044,0.00005939212,0.0001022459,0.0001781806,0.0001250806,0.000007618325],"category_scores_gemma":[0.0001165735,0.0001633721,0.00005025482,0.0003074036,0.00001584519,0.001856711,0.00002173184,0.00005291796,0.0001231093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004614336,"about_ca_system_score_gemma":0.00002556074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001785045,"about_ca_topic_score_gemma":0.00001525681,"domain_scores_codex":[0.9984199,0.0000198246,0.0009630987,0.0001753579,0.0001367652,0.0002850343],"domain_scores_gemma":[0.9989537,0.0001008992,0.000195167,0.0002686181,0.0004057946,0.00007583852],"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":[0.0001249467,0.00003003747,0.0002579715,0.001069737,0.0000357588,9.17175e-8,0.004504292,0.9229127,0.02645974,0.003262805,0.001102982,0.0402389],"study_design_scores_gemma":[0.001677893,0.000614664,0.0002610632,0.0027143,0.00001814618,0.00003122238,0.00844832,0.7566192,0.09331131,0.0002752438,0.1347535,0.001275142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02303733,0.0000786203,0.9709153,0.00005047958,0.001635068,0.003681295,0.00007083428,0.0002584016,0.0002726726],"genre_scores_gemma":[0.987765,0.00001903956,0.006545443,0.00002015654,0.00008170287,0.005365612,0.00008585844,0.00002589271,0.00009129968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9647276,"threshold_uncertainty_score":0.6662122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242880466948806,"score_gpt":0.2512498827218579,"score_spread":0.2388210780523698,"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."}}