{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004564401,0.0005078221,0.0004917699,0.0001873039,0.0002034865,0.0005629736,0.0003651981,0.0004991638,0.001566697],"category_scores_gemma":[0.001375925,0.0002014501,0.0002075814,0.0002087358,0.0004223957,0.0009551512,0.0007671107,0.0005142576,0.000416008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002807576,"about_ca_system_score_gemma":0.0002207724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002240149,"about_ca_topic_score_gemma":0.0004812562,"domain_scores_codex":[0.9994664,0.0001690379,0.00002074327,0.00007704091,0.0002233319,0.00004361991],"domain_scores_gemma":[0.9993801,0.0003830763,0.00005878526,0.00006355735,0.0000983401,0.00001621757],"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":[0.0003360839,0.00007898182,0.001639685,0.0002252863,0.0001030888,0.0002178758,0.0001216322,0.5916945,0.1102529,0.1300532,0.001701709,0.163575],"study_design_scores_gemma":[0.00001657125,0.00008375951,0.000230187,0.00001122052,0.000009132614,0.0001173175,0.00001347037,0.9746558,0.01364007,0.009894918,0.001313809,0.00001372179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01931893,0.0001978116,0.9783128,0.0001065948,0.00002367973,0.00001061841,0.00002096642,0.0001330451,0.001875437],"genre_scores_gemma":[0.7547238,0.000348771,0.2418689,0.0002971306,0.00006313317,0.00004657519,0.00006614407,0.00002613895,0.002559483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001566697,"threshold_uncertainty_score":0.005241156,"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."}}