{"id":"W2301261200","doi":"10.1155/2016/9767065","title":"SDN Controlled mmWave Massive MIMO Hybrid Precoding for 5G Heterogeneous Mobile Systems","year":2016,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; École de Technologie Supérieure; Université du Québec à Montréal","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Precoding; Computer science; Heterogeneous network; Beamforming; MIMO; Channel state information; Computer network; Interference (communication); Radio resource management; Spectral efficiency; Channel (broadcasting); Wireless; Telecommunications; Wireless network","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":[0.0005582789,0.0003056978,0.0005680022,0.0002710549,0.0001445094,0.0002939905,0.0002430322,0.0001302438,0.00003325707],"category_scores_gemma":[0.00007975663,0.0002258207,0.0001989609,0.00009061571,0.00002047656,0.0009926719,0.00002452261,0.00008087743,0.0003450637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000281004,"about_ca_system_score_gemma":0.0000316384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001898942,"about_ca_topic_score_gemma":0.000001101411,"domain_scores_codex":[0.9977262,0.0000615462,0.00127907,0.0001824452,0.0003157448,0.0004349732],"domain_scores_gemma":[0.9985104,0.0001881461,0.0003285402,0.0004560428,0.0003649391,0.0001519135],"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":[0.00009447041,0.0000133757,0.00002467098,0.0009093533,0.0001903149,0.000001552067,0.0005931027,0.9784909,0.009703543,0.00009560545,0.002157847,0.007725248],"study_design_scores_gemma":[0.003927096,0.0001475795,0.000001052569,0.0003343918,0.00003281108,0.00005526314,0.0004302143,0.9099863,0.02863824,0.000009483502,0.05604335,0.0003942513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152533,0.001166898,0.8314372,0.000009688148,0.003549981,0.006826775,0.0003106996,0.0007483332,0.00341747],"genre_scores_gemma":[0.990578,0.0001020521,0.0001749102,0.00003256309,0.0002688969,0.008349195,0.00008335082,0.00004768671,0.0003633489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.838045,"threshold_uncertainty_score":0.9208699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01308487893169656,"score_gpt":0.2149881826723079,"score_spread":0.2019033037406113,"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."}}