{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007069578,0.0005304878,0.0004015474,0.0003391802,0.0001918188,0.0005529636,0.0004842277,0.0005881944,0.00100912],"category_scores_gemma":[0.002029612,0.0001797952,0.0002611442,0.0005115952,0.0003906295,0.00080651,0.0004339613,0.0004112255,0.0002506246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000312527,"about_ca_system_score_gemma":0.0006046869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109757,"about_ca_topic_score_gemma":0.001546379,"domain_scores_codex":[0.9995549,0.0001679843,0.00002406123,0.00005763756,0.0001625564,0.00003279269],"domain_scores_gemma":[0.9996014,0.0002351817,0.00004059417,0.00002922865,0.00008632993,0.000007291159],"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":[0.0001529614,0.00004921502,0.001078376,0.0001777316,0.00006114841,0.00005763435,0.0000765299,0.8034559,0.01301337,0.03166062,0.001117352,0.1490992],"study_design_scores_gemma":[0.000008666572,0.00009815949,0.0002900191,0.00001735572,0.0000121162,0.00004445814,0.00001832032,0.9891863,0.004246961,0.004919881,0.001149855,0.000007755977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01261493,0.0005571154,0.9844164,0.0001283589,0.0000275858,0.00001539221,0.00001359584,0.00008083328,0.00214581],"genre_scores_gemma":[0.6426234,0.001380076,0.3519925,0.0001213333,0.00006551335,0.00009678534,0.00007121037,0.00003226029,0.00361709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001109757,"threshold_uncertainty_score":0.003738761,"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."}}