{"id":"W2166552116","doi":"10.1109/isit.2009.5205768","title":"Noisy feedback linear precoding: A Bayesian Cram&amp;#x00E9;r-Rao bound","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; Fading; Channel state information; Transmitter; Upper and lower bounds; Mathematics; Rician fading; Zero-forcing precoding; Cramér–Rao bound; Computer science; Additive white Gaussian noise; Gaussian; Channel (broadcasting); Algorithm; Control theory (sociology); Telecommunications; MIMO; Decoding methods; Estimation theory; Wireless; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008892089,0.0002300979,0.0002295668,0.0001039834,0.00007312704,0.00006276299,0.0001505246,0.0001311914,0.0002597047],"category_scores_gemma":[0.0000418519,0.0002253999,0.00006503594,0.0002636233,0.00001453137,0.0003443527,0.00001387643,0.0001509591,0.0003751558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000116685,"about_ca_system_score_gemma":0.00001313605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008530139,"about_ca_topic_score_gemma":0.00004358468,"domain_scores_codex":[0.9989461,0.00001461957,0.0003236989,0.0002346911,0.0001256449,0.0003552519],"domain_scores_gemma":[0.9993885,0.00002834465,0.00003739572,0.0003642157,0.00005557283,0.0001259385],"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":[0.00001237629,0.00005233059,0.0001300215,0.00008466443,0.00004071891,0.0000048939,0.0005835675,0.9700961,0.00951724,0.001529382,0.007491903,0.01045687],"study_design_scores_gemma":[0.001230587,0.0001290799,0.0004397763,0.0001531063,0.00003882713,0.00005545816,0.0001460802,0.8654211,0.006472812,0.001643163,0.1230851,0.001184917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003760059,0.0001605061,0.9064159,0.0001509381,0.0003547909,0.0002903095,0.000003584742,0.001144161,0.08771969],"genre_scores_gemma":[0.8324695,0.00006075118,0.1580258,0.0001545542,0.0003467607,0.00001522518,0.00003258458,0.00007036026,0.008824421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8287095,"threshold_uncertainty_score":0.9191541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136523393672755,"score_gpt":0.2420141119811008,"score_spread":0.2283617726138253,"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."}}