{"id":"W2114170946","doi":"10.1109/icc.2007.721","title":"MIMO LMMSE Transceiver Design with Imperfect CSI at Both Ends","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Minimum mean square error; MIMO; Channel state information; Computer science; Mean squared error; Control theory (sociology); Channel (broadcasting); Estimator; Precoding; Transmitter power output; Mathematics; Telecommunications; Statistics; Transmitter; Wireless","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":[0.001725655,0.00117902,0.001254981,0.0004365022,0.0003701228,0.001057637,0.0008964963,0.001257284,0.002241069],"category_scores_gemma":[0.004256284,0.000536752,0.0005135552,0.0005307493,0.000691134,0.001564292,0.0009963765,0.0008319014,0.001015663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005478329,"about_ca_system_score_gemma":0.0008045898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004094201,"about_ca_topic_score_gemma":0.0006215166,"domain_scores_codex":[0.9983772,0.0005209162,0.0001019542,0.0002801636,0.0006041351,0.000115537],"domain_scores_gemma":[0.9987996,0.0005594649,0.0001801852,0.0001386164,0.0002892046,0.00003302286],"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":[0.0001998272,0.00009124726,0.0008641453,0.0003504446,0.0001248778,0.0001565461,0.0003145954,0.7945518,0.02501953,0.07008151,0.002143365,0.1061022],"study_design_scores_gemma":[0.00003618968,0.0001845363,0.0002710163,0.00003136254,0.0000374393,0.0002247335,0.00004223395,0.9679784,0.01325209,0.01450754,0.00340405,0.0000304247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003534876,0.0001303056,0.9946383,0.0001029607,0.00001737652,0.00001776986,0.00003042027,0.00006803197,0.001459935],"genre_scores_gemma":[0.4347463,0.0006934606,0.5562191,0.0003129289,0.0001973832,0.0003330453,0.0002818646,0.00008928113,0.007126682],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002241069,"threshold_uncertainty_score":0.009126246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008839658475158038,"score_gpt":0.2041272736166382,"score_spread":0.1952876151414802,"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."}}