{"id":"W3013263387","doi":"10.1109/icnc47757.2020.9049735","title":"A Deep Learning Based Channel Estimation for High Mobility Vehicular Communications","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Computing, Networking and Communications (ICNC)","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Kalman filter; Channel (broadcasting); Computer science; Algorithm; Minimum mean square error; Covariance; Doppler effect; Covariance matrix; Degradation (telecommunications); Artificial intelligence; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006389599,0.0002712937,0.0002858943,0.0001301285,0.000977796,0.0005311914,0.003426074,0.0001259789,0.00001360948],"category_scores_gemma":[0.0002955732,0.0003065993,0.0001053477,0.0005092003,0.0002099195,0.0003241586,0.001041768,0.0005655014,0.00002487438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122419,"about_ca_system_score_gemma":0.0001178361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003491991,"about_ca_topic_score_gemma":0.00001781789,"domain_scores_codex":[0.9975882,0.0004498783,0.0006323537,0.0006480473,0.0003997424,0.0002817647],"domain_scores_gemma":[0.9958962,0.001023645,0.0005390765,0.001705622,0.0006588601,0.0001765744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005285002,0.000323686,0.0005084196,0.00004275942,0.0001039082,9.456037e-7,0.001610371,0.2619439,0.0003036729,0.5703762,0.0003689665,0.1643643],"study_design_scores_gemma":[0.0005685563,0.0001279586,0.002241224,0.0001210723,0.00001878752,0.000002783298,0.00009981387,0.9843681,0.00005240238,0.008144435,0.00396646,0.0002884492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001960972,0.0002467177,0.9436368,0.05171475,0.0002851548,0.0005505261,0.000008416663,0.0004134469,0.001183266],"genre_scores_gemma":[0.8925822,0.0002078927,0.1052926,0.00115077,0.000185139,0.0001459313,0.0003891084,0.00002285086,0.00002356221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8906212,"threshold_uncertainty_score":0.9999386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09520587541893687,"score_gpt":0.3087853311723238,"score_spread":0.2135794557533869,"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."}}