{"id":"W3116062642","doi":"10.1109/lcomm.2020.3047994","title":"Detection for Hybrid Beamforming Millimeter Wave Massive MIMO Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; MIMO; Computer science; Precoding; Channel (broadcasting); Extremely high frequency; Electronic engineering; Computational complexity theory; Detector; Channel state information; Telecommunications; Algorithm; Wireless; Engineering","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":[0.0001169248,0.0001556505,0.0001657071,0.00009425579,0.0002196616,0.00007474001,0.0003587197,0.00004355265,0.000004786401],"category_scores_gemma":[0.00003261141,0.0001725346,0.0001047427,0.0001303926,0.00003924057,0.0001729329,0.00004787727,0.000202766,0.00003396378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008727238,"about_ca_system_score_gemma":0.000007262057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001217644,"about_ca_topic_score_gemma":0.000003705422,"domain_scores_codex":[0.9991533,0.00004656838,0.000330988,0.0001628074,0.00009020072,0.0002161401],"domain_scores_gemma":[0.9989841,0.0001257041,0.00006320234,0.000658743,0.00007019119,0.00009803204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006394906,0.00000843255,0.000004055639,0.0001085876,0.00008302991,5.036237e-7,0.0005782533,0.06862283,0.9200752,0.00003061864,0.001882347,0.008599697],"study_design_scores_gemma":[0.0002460072,0.00001953053,0.000001382389,0.00002711902,0.0000352148,0.000003897274,0.00008977257,0.7976853,0.1871538,0.00002204628,0.0145234,0.0001925144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04775124,0.000484798,0.9476073,0.002580497,0.0004076399,0.0005176218,0.00004781032,0.0003374593,0.0002655792],"genre_scores_gemma":[0.9756842,0.0001282841,0.02221391,0.001422272,0.0001550284,0.0002644051,0.00006952247,0.00005295634,0.000009429542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.927933,"threshold_uncertainty_score":0.7035755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0618964789667658,"score_gpt":0.2397026670872917,"score_spread":0.1778061881205259,"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."}}