{"id":"W2010486721","doi":"10.1155/2014/720546","title":"Analysis of MIMO Receiver Using Generalized Least Squares Method in Colored Environments","year":2014,"lang":"en","type":"article","venue":"Journal of Computer Networks and Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"MIMO; Colors of noise; Algorithm; Estimator; Noise (video); Mathematics; Minimum mean square error; Gaussian noise; Computer science; Covariance; Covariance matrix; Colored; Statistics; Channel (broadcasting); Telecommunications; White noise","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":[0.000327424,0.00007350488,0.0003194992,0.0002421238,0.00003920752,0.00001484928,0.0002344804,0.00004805974,0.000003105192],"category_scores_gemma":[0.000005065081,0.00007185938,0.00006994193,0.0003548253,0.00003031277,0.0001207048,0.00006587352,0.0001346107,7.502472e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003886347,"about_ca_system_score_gemma":0.000004199318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001231096,"about_ca_topic_score_gemma":0.0000298071,"domain_scores_codex":[0.999129,0.0002074412,0.0004705893,0.00005000195,0.00006387888,0.00007909095],"domain_scores_gemma":[0.9991972,0.0001639076,0.0002362221,0.0003272533,0.00003887502,0.00003657207],"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":[0.000005499079,0.00002122544,0.002775866,0.000004243936,0.0002184677,1.809718e-7,0.0001521051,0.988565,0.0001874658,0.0001508831,0.00001877033,0.007900286],"study_design_scores_gemma":[0.0003250694,0.00002104432,0.006837842,0.00004955247,0.0001774587,0.000005071593,0.00001949725,0.9918056,0.00001168037,0.00004756087,0.000638608,0.00006102855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03143775,0.001281884,0.9670844,0.00002579859,0.00006650836,0.00005015076,0.000001105328,0.000005420704,0.00004694933],"genre_scores_gemma":[0.7025011,0.0008850801,0.2965511,0.00001331712,0.00003348914,0.000001072212,0.000004899561,0.000007615733,0.000002368843],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6710633,"threshold_uncertainty_score":0.293034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01722209416301814,"score_gpt":0.2663126557333785,"score_spread":0.2490905615703604,"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."}}