{"id":"W4386243327","doi":"10.18280/mmep.100427","title":"Minimum Mean Square Error Algorithm for Improving Spectral Efficiency by Reducing Power Consumption of Beamforming in 5G Networks","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Minimum mean square error; MATLAB; Mean squared error; Algorithm; Precoding; Computer science; Kalman filter; Beamforming; Power (physics); Spectral density; Spectral efficiency; Mathematical optimization; Mathematics; Statistics; Telecommunications; Artificial intelligence; MIMO","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003875045,0.0002087556,0.0003255569,0.0001914623,0.0000420498,0.00002733924,0.00007768381,0.0001255489,0.00000218054],"category_scores_gemma":[0.00002771892,0.0002198617,0.00004991476,0.0002613324,0.00001940963,0.0001442365,0.00001708122,0.000164273,0.000001799049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005969032,"about_ca_system_score_gemma":0.00000470799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007875507,"about_ca_topic_score_gemma":5.434928e-7,"domain_scores_codex":[0.9986928,0.000006106679,0.0005343499,0.0002302976,0.0001078222,0.0004286508],"domain_scores_gemma":[0.9995508,0.0001549761,0.00005496724,0.0001397141,0.00002813687,0.00007140513],"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":[0.000001715448,0.00001456016,0.000003358852,0.001472932,0.000009706871,5.561807e-7,0.001024525,0.992303,0.00337702,0.0002875359,0.000005697497,0.001499395],"study_design_scores_gemma":[0.000312921,0.00003758106,0.000001138091,0.0006270333,0.00001243937,0.000005067321,0.0000923823,0.9973424,0.0009451617,0.0003915764,0.000006147027,0.0002261429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06796282,0.0004294904,0.9305511,0.000003998879,0.0001396936,0.0004807498,0.00001021014,0.0003923488,0.00002957846],"genre_scores_gemma":[0.8515601,0.00005049444,0.1481451,7.832089e-7,0.00002875894,0.00009405971,0.00002108066,0.00007530289,0.00002436487],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7835972,"threshold_uncertainty_score":0.8965698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154049914115114,"score_gpt":0.2198769554861465,"score_spread":0.2044719640746351,"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."}}