{"id":"W3214484149","doi":"10.1109/lcomm.2021.3127160","title":"Deep Learning-Based Time-Varying Channel Estimation for RIS Assisted Communication","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Channel (broadcasting); Artificial intelligence; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002649379,0.0002416326,0.000274654,0.0002237638,0.0007174248,0.0001012608,0.00170559,0.0001577929,0.00001972593],"category_scores_gemma":[0.0003677745,0.00031037,0.0001303851,0.0006324528,0.0002476186,0.0003315595,0.000231185,0.0005996136,0.00007780303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002546204,"about_ca_system_score_gemma":0.00003176717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006205912,"about_ca_topic_score_gemma":0.00002557688,"domain_scores_codex":[0.9985864,0.0002483708,0.0004786334,0.0002274053,0.0001569833,0.0003022603],"domain_scores_gemma":[0.9943368,0.00114159,0.0001718841,0.004051214,0.0002440467,0.00005439277],"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":[0.000005267952,0.00006523431,0.00002132651,0.00004978589,0.00005579486,4.889117e-7,0.0001792932,0.9273681,0.04185759,0.0002666807,0.001198682,0.02893174],"study_design_scores_gemma":[0.0005324854,0.00001197165,0.0001572778,0.00009468623,0.00003074553,0.000005907762,0.0001081995,0.9622789,0.02816087,0.00048317,0.007810947,0.0003248219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003798913,0.003143793,0.9795123,0.009998951,0.00009495211,0.0004369926,0.00001582012,0.002297115,0.0007011002],"genre_scores_gemma":[0.7273194,0.0008645169,0.2697742,0.0004153881,0.00001134885,0.0006287002,0.0008646604,0.00007492198,0.00004684895],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7235205,"threshold_uncertainty_score":0.9999349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559352699645717,"score_gpt":0.2659061862021836,"score_spread":0.2403126592057264,"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."}}