{"id":"W4312975881","doi":"10.1109/tvt.2022.3231727","title":"Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Channel (broadcasting); Communications system; Interference (communication); Base station; Convolutional neural network; Deep learning; Real-time computing; Electronic engineering; Signal-to-noise ratio (imaging); Wireless; Artificial intelligence; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001730563,0.0001852529,0.0002309787,0.0006486473,0.0008818255,0.00002212394,0.0003278808,0.000212373,0.000003986738],"category_scores_gemma":[0.00002276137,0.0002211999,0.00005365738,0.0007379521,0.0001324436,0.00009838436,0.00001329137,0.0009237995,0.000002242207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004173686,"about_ca_system_score_gemma":0.00001095492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009743792,"about_ca_topic_score_gemma":0.00002115573,"domain_scores_codex":[0.9990815,0.00008466638,0.0002857297,0.0002164425,0.0001096865,0.0002219924],"domain_scores_gemma":[0.9989995,0.0001580025,0.00008751865,0.0006499853,0.00008082591,0.00002417756],"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":[0.0000110814,0.00002011333,8.974933e-7,0.00004641155,0.00004212143,0.000001335171,0.00008694425,0.7581834,0.005342362,0.0007201912,0.000005279156,0.2355399],"study_design_scores_gemma":[0.0004123107,0.0001028285,0.00000635677,0.00005378599,0.00002888218,0.0001046839,0.004174631,0.9616567,0.03163264,0.0006071778,0.001019509,0.000200434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03440677,0.0007602904,0.9591781,0.0004203712,0.0001255305,0.0004638744,0.00001316204,0.004595596,0.00003626133],"genre_scores_gemma":[0.9551089,0.0001992985,0.04404623,0.00001131243,0.000002080459,0.000526139,0.00004361645,0.00004457777,0.00001786034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9207021,"threshold_uncertainty_score":0.9020272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056400367975095,"score_gpt":0.2216626231313627,"score_spread":0.2110986194516118,"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."}}