{"id":"W1968787329","doi":"10.1109/tim.2011.2170915","title":"Distributed Spatiotemporal Neural Network for Nonlinear Dynamic Transmitter Modeling and Adaptive Digital Predistortion","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Predistortion; Baseband; Computer science; Amplifier; Electronic engineering; Transmitter; Linearization; Artificial neural network; Doherty amplifier; Adaptive filter; Nonlinear system; Wideband; Algorithm; RF power amplifier; Artificial intelligence; Engineering; CMOS; Telecommunications","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":[0.0001549785,0.0003476579,0.0002115844,0.0001511033,0.0001428441,0.000266422,0.0004890472,0.0004130607,0.001017662],"category_scores_gemma":[0.0003576168,0.0001410355,0.0003113045,0.0002156538,0.000189114,0.0004283668,0.0002093914,0.0004908717,0.0002231198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103074,"about_ca_system_score_gemma":0.0002306395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002481101,"about_ca_topic_score_gemma":0.003044278,"domain_scores_codex":[0.9999404,0.0000140571,0.000004086638,0.00001581499,0.00002040885,0.000005331252],"domain_scores_gemma":[0.9999378,0.00002681125,0.000009315409,0.000006918433,0.00001706118,0.000002193916],"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":[0.00002818348,0.00001264749,0.0003012819,0.00004298173,0.00002082051,0.00006721104,0.00003272879,0.9488362,0.009062025,0.009090484,0.0002323007,0.03227317],"study_design_scores_gemma":[6.013062e-7,0.000004951711,0.00003207526,0.000001104102,0.000001851091,0.000007904636,0.000001254917,0.998742,0.0004335673,0.0005661799,0.000207428,0.000001147215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01030803,0.0002552497,0.9871877,0.0000562328,0.00002317889,0.00001157621,0.00003093694,0.0001426578,0.001984487],"genre_scores_gemma":[0.8437485,0.000961623,0.1472744,0.00006262191,0.00004825953,0.0001494976,0.0001262942,0.00003772073,0.007591219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002481101,"threshold_uncertainty_score":0.004933298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04786666618618762,"score_gpt":0.2286015724576078,"score_spread":0.1807349062714202,"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."}}