{"id":"W2963624992","doi":"10.1109/tcomm.2018.2831222","title":"Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Precoding; Channel (broadcasting); MIMO; Computer science; Channel state information; Spatial correlation; Electronic engineering; Radio frequency; Algorithm; Telecommunications; Engineering; Wireless","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001285071,0.0001873065,0.0002386853,0.0002490996,0.0006144395,0.0000677628,0.0006531607,0.0001062563,0.000008174631],"category_scores_gemma":[0.00004768128,0.000216425,0.00007689146,0.0004282565,0.0003906006,0.000543677,0.00001075609,0.0002628223,0.00001315261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009508516,"about_ca_system_score_gemma":0.00004298649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009037121,"about_ca_topic_score_gemma":0.0000416551,"domain_scores_codex":[0.9990033,0.00004598081,0.0004758546,0.0001753631,0.00009889944,0.0002006244],"domain_scores_gemma":[0.9971563,0.0005050248,0.0001495472,0.001742378,0.0003735267,0.00007321568],"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":[0.00006863165,0.0005687273,0.00001138236,0.0004270218,0.0002971296,2.137472e-7,0.004217466,0.8439401,0.002587238,0.005077069,0.0003159757,0.1424891],"study_design_scores_gemma":[0.000332393,0.00009248981,0.00001002018,0.0002655804,0.00007447468,0.000006431488,0.0003436501,0.9866191,0.007146569,0.004238705,0.0006518047,0.0002188392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004555926,0.0004625208,0.9959421,0.0004600064,0.0001514508,0.000687749,0.0002610915,0.0003168984,0.001262615],"genre_scores_gemma":[0.8147686,0.0003800298,0.1842437,0.00002081349,0.00002200694,0.0003840202,0.00007374705,0.00004843823,0.00005864897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.814313,"threshold_uncertainty_score":0.8825554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849990815604082,"score_gpt":0.2878402276839752,"score_spread":0.2593403195279343,"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."}}