{"id":"W2162708855","doi":"10.1109/tmtt.2010.2098041","title":"Physically Inspired Neural Network Model for RF Power Amplifier Behavioral Modeling and Digital Predistortion","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Predistortion; Adjacent channel power ratio; Amplifier; Wideband; Behavioral modeling; Electronic engineering; Artificial neural network; Computer science; RF power amplifier; Linearization; Adjacent channel; Bandwidth (computing); Mean squared error; Nonlinear system; Engineering; Control theory (sociology); Artificial intelligence; CMOS; Telecommunications; Mathematics","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":[0.0002198463,0.0005647646,0.0003810294,0.0002789079,0.0002346022,0.0004495587,0.001018103,0.000941072,0.001893499],"category_scores_gemma":[0.0005231468,0.0002866474,0.0004985867,0.0002849162,0.0002811701,0.00074062,0.000271107,0.0008460802,0.0004955314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005436248,"about_ca_system_score_gemma":0.0004161373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003527149,"about_ca_topic_score_gemma":0.003607319,"domain_scores_codex":[0.9998872,0.00002676585,0.000005776874,0.00002845004,0.00003824544,0.00001350816],"domain_scores_gemma":[0.9998937,0.00004700631,0.00001294651,0.000008226087,0.00003464085,0.000003431234],"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":[0.00001665943,0.00001261246,0.0001553154,0.00003060085,0.00001369048,0.00004957729,0.00002153014,0.9831182,0.00250136,0.005350017,0.0002513043,0.008479127],"study_design_scores_gemma":[7.25204e-7,0.000003403496,0.00003439232,0.000001462543,0.000001693226,0.000007024888,0.000001021001,0.9988272,0.0002329542,0.0006640674,0.0002245752,0.00000149463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01631436,0.0003796068,0.9756664,0.000162198,0.00006696849,0.00003084432,0.0001161766,0.0003080847,0.006955428],"genre_scores_gemma":[0.8568822,0.001008257,0.1146446,0.0001428358,0.00007011665,0.000368228,0.0003416528,0.000090583,0.02645171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003527149,"threshold_uncertainty_score":0.007013202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02835237584316867,"score_gpt":0.2431280108400427,"score_spread":0.214775634996874,"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."}}