{"id":"W2510675935","doi":"10.1109/spawc.2016.7536763","title":"Hybrid analog and digital beamforming for OFDM-based large-scale MIMO systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Beamforming; Orthogonal frequency-division multiplexing; Computer science; Electronic engineering; MIMO; Broadband; Multiplexing; Spectral efficiency; WSDMA; MIMO-OFDM; Precoding; Engineering; Telecommunications; Channel (broadcasting)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003167979,0.0007351208,0.0003431166,0.0002758053,0.0002935919,0.0004962651,0.0004212816,0.0004691171,0.002336784],"category_scores_gemma":[0.0007334921,0.0002873101,0.0002912829,0.0005465134,0.0004396358,0.0007635971,0.0005250704,0.0003478178,0.000505058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003402032,"about_ca_system_score_gemma":0.0003282655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005647528,"about_ca_topic_score_gemma":0.001456544,"domain_scores_codex":[0.9997793,0.00007838276,0.00001016916,0.00003210423,0.00007706657,0.00002295272],"domain_scores_gemma":[0.9996762,0.0001741482,0.00004450731,0.00002633869,0.00005784379,0.00002109732],"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":[0.00009639303,0.00003115564,0.000351573,0.00009185982,0.00003495328,0.00006285672,0.00004909677,0.825617,0.02257475,0.01982716,0.0008758044,0.1303874],"study_design_scores_gemma":[0.00001366469,0.00005916816,0.0001106936,0.000006890072,0.000007898338,0.00003752827,0.00001555507,0.9891431,0.003292896,0.006165463,0.001137142,0.00001004978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004733584,0.00009187868,0.9934598,0.00004753307,0.000008134012,0.00001208165,0.00001011125,0.00007999171,0.00155686],"genre_scores_gemma":[0.5497739,0.0003953282,0.4462576,0.0001080849,0.00004657667,0.0001176439,0.00006885969,0.00003602088,0.003196045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002336784,"threshold_uncertainty_score":0.007817268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01174387212077154,"score_gpt":0.2014316342622635,"score_spread":0.1896877621414919,"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."}}