{"id":"W2950773888","doi":"10.48550/arxiv.1407.0446","title":"Beamforming for Multiuser Massive MIMO Systems: Digital versus Hybrid Analog-Digital","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Beamforming; Multiplexing; MIMO; Telecommunications link; Electronic engineering; Maximization; Transmitter; Computer science; Block (permutation group theory); Converters; Radio frequency; Algorithm; Channel (broadcasting); Mathematics; Telecommunications; Mathematical optimization; Engineering; Electrical engineering","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":[0.0004709463,0.000632497,0.0002866496,0.0002708056,0.0002313778,0.0006696934,0.0003388351,0.0006030587,0.002128118],"category_scores_gemma":[0.001157533,0.0001949088,0.0002538346,0.0004313492,0.0005277019,0.0008737177,0.0005908044,0.0004413385,0.0005009045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003012168,"about_ca_system_score_gemma":0.000219968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002990199,"about_ca_topic_score_gemma":0.0005773762,"domain_scores_codex":[0.9996961,0.00010354,0.00001524985,0.0000453321,0.0001164205,0.00002328883],"domain_scores_gemma":[0.9995376,0.0002848167,0.00004936945,0.00004037318,0.00006406246,0.00002385528],"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":[0.0002727534,0.00009111164,0.001177793,0.0004086521,0.0001084813,0.0001640468,0.0001110665,0.5135604,0.07124669,0.1405789,0.001453807,0.2708263],"study_design_scores_gemma":[0.00003273942,0.0003301917,0.0004490743,0.00005660546,0.00003412267,0.0002592391,0.00004871934,0.9460179,0.01540836,0.03125796,0.006070158,0.00003498634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01227526,0.0007064986,0.9801801,0.000256652,0.00005681011,0.00002072125,0.00002167327,0.00006692245,0.006415411],"genre_scores_gemma":[0.6515386,0.002402099,0.3388747,0.0005127122,0.0003039406,0.0001011338,0.00007754762,0.00003080219,0.006158502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002128118,"threshold_uncertainty_score":0.007119298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0384099572874222,"score_gpt":0.1784676404233065,"score_spread":0.1400576831358843,"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."}}