{"id":"W2921796004","doi":"10.1109/tvt.2020.3015815","title":"Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Precoding; MIMO; Matching pursuit; Channel sounding; Channel (broadcasting); Beamforming; Overhead (engineering); Multiplexing; Channel state information","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":[0.00007106762,0.0001583839,0.0001850469,0.0001963805,0.0002191015,0.00002458825,0.0001817301,0.0001347087,0.000007540786],"category_scores_gemma":[0.00001288054,0.0001807666,0.00006601014,0.0002619167,0.00007195667,0.0000804139,0.000001534644,0.0002662221,0.000006540842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000045136,"about_ca_system_score_gemma":0.00001460639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001651558,"about_ca_topic_score_gemma":0.000004403221,"domain_scores_codex":[0.999272,0.00001928855,0.0002372851,0.0002129689,0.00007067023,0.0001877992],"domain_scores_gemma":[0.9994375,0.00006370441,0.00003779846,0.0003233363,0.00006455784,0.00007310184],"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":[0.00002299024,0.00006445127,8.511644e-7,0.0001233293,0.00006658883,0.000001082317,0.00007980871,0.789578,0.1667499,0.00004936,0.00004170608,0.04322197],"study_design_scores_gemma":[0.0004847524,0.0000555975,3.200771e-7,0.00002323551,0.00003493222,0.00000340767,0.00002943498,0.6121772,0.3866878,0.0001386171,0.0002552105,0.0001095211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04168069,0.0001174414,0.9535643,0.002958076,0.00008827093,0.0004796425,0.0001920656,0.000907849,0.00001161816],"genre_scores_gemma":[0.9784459,0.00007609472,0.02092786,0.0001311177,0.00001000993,0.0002936466,0.00007827296,0.0000353571,0.00000174656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9367652,"threshold_uncertainty_score":0.7371449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056090019634306,"score_gpt":0.2401796331013558,"score_spread":0.2096187329050127,"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."}}