{"id":"W2111097751","doi":"10.1109/pacrim.2009.5291394","title":"Improving robustness of precoded MIMO systems with imperfect channel estimation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Robustness (evolution); Computer science; MIMO; Imperfect; Precoding; Channel (broadcasting); Control theory (sociology); Bit error rate; Overhead (engineering); Word error rate; Telecommunications; Speech recognition; Artificial intelligence","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":[0.001410567,0.000771255,0.0006570379,0.0004518257,0.0002163519,0.0009611768,0.0004322636,0.0007509181,0.0006195699],"category_scores_gemma":[0.01137463,0.0003235679,0.0002067142,0.000341316,0.0007174074,0.001202234,0.0008537573,0.0006547254,0.0002385351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004870749,"about_ca_system_score_gemma":0.0003753057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009713147,"about_ca_topic_score_gemma":0.0008875237,"domain_scores_codex":[0.9984258,0.0004971361,0.00009094705,0.0001891552,0.0006310063,0.0001658995],"domain_scores_gemma":[0.9953632,0.003165073,0.0005186032,0.0004157853,0.0004992246,0.00003799963],"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":[0.0002588578,0.00002208587,0.0009076774,0.00009448298,0.00005186076,0.0002456796,0.0001367308,0.9194509,0.03950612,0.008157901,0.0002565755,0.03091102],"study_design_scores_gemma":[0.000009343684,0.00008391922,0.000475103,0.00002067675,0.0000164497,0.0001002205,0.00001682378,0.9830297,0.01358694,0.002336622,0.0003004747,0.00002377466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1909755,0.001150058,0.8037271,0.0003077936,0.0000583129,0.0000228271,0.0001073049,0.0005530495,0.003097949],"genre_scores_gemma":[0.9788368,0.0004251155,0.01971379,0.00004858095,0.00003577744,0.00001925874,0.00005217913,0.00002680859,0.0008416845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001410567,"threshold_uncertainty_score":0.007459939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008073848198193147,"score_gpt":0.2192702542061041,"score_spread":0.211196406007911,"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."}}