{"id":"W2045153903","doi":"10.1186/1687-6180-2014-85","title":"MMSE precoding for multiuser MISO downlink transmission with non-homogeneous user SNR conditions","year":2014,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Telecommunications link; Base station; Channel state information; Computer science; Quantization (signal processing); Algorithm; Zero-forcing precoding; Minimum mean square error; Control theory (sociology); Transmission (telecommunications); Channel (broadcasting); Mathematics; MIMO; Estimator; Mathematical optimization; Wireless; Statistics; Telecommunications","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":[0.0004571351,0.0005946808,0.0005411913,0.0002030199,0.0002130732,0.0005697582,0.0004234986,0.0004034706,0.001041723],"category_scores_gemma":[0.001798058,0.00022923,0.0003459385,0.000354358,0.0004159953,0.0004718209,0.0004864592,0.00042853,0.0003854567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003468786,"about_ca_system_score_gemma":0.0006846775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001827058,"about_ca_topic_score_gemma":0.003125731,"domain_scores_codex":[0.9995964,0.000120874,0.00002197937,0.00008038985,0.0001284087,0.00005189696],"domain_scores_gemma":[0.9996033,0.0001771505,0.0000642305,0.00005908721,0.0000829748,0.00001333441],"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":[0.0001954819,0.00005206166,0.001092111,0.0002013629,0.00008593009,0.0002183944,0.0002163693,0.8250439,0.02686948,0.03295372,0.001387913,0.1116833],"study_design_scores_gemma":[0.00001084214,0.0001141749,0.0003425158,0.00001327805,0.00001781526,0.00007010449,0.00003085638,0.9868526,0.006790732,0.004614842,0.001131381,0.00001094333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02148543,0.000241625,0.9760934,0.00007241958,0.00002246708,0.00002247902,0.00005832294,0.0001199444,0.001883912],"genre_scores_gemma":[0.8325583,0.000649647,0.1619693,0.0001390313,0.00007044917,0.00009180194,0.0001810573,0.00002812663,0.004312305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001827058,"threshold_uncertainty_score":0.003632843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007836762995809295,"score_gpt":0.2602354879918941,"score_spread":0.2523987249960847,"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."}}