{"id":"W4409581412","doi":"10.1109/tcomm.2025.3562320","title":"Channel Estimation and Localization for Cylindrical RIS-Assisted Multi-User ISAC Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Queen's University Belfast; Queen's University; European Commission; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; Leverhulme Trust; National Science Foundation","keywords":"Computer science; Channel (broadcasting); Electronic engineering; Electrical engineering; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003763465,0.0008487199,0.0006021168,0.00032742,0.0003345238,0.0006478164,0.0006263896,0.0006323659,0.0007810341],"category_scores_gemma":[0.001521028,0.0002553733,0.0004345468,0.0005139234,0.0007871887,0.001085989,0.0007926807,0.0007084085,0.0003119135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677008,"about_ca_system_score_gemma":0.0006848423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003118129,"about_ca_topic_score_gemma":0.002957817,"domain_scores_codex":[0.9993788,0.0001831917,0.00001864575,0.0001378328,0.0001868312,0.00009459418],"domain_scores_gemma":[0.9993037,0.0002707086,0.000116234,0.0001082078,0.0001642382,0.00003679571],"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":[0.0003646692,0.00007201947,0.00285587,0.0002427768,0.00009152883,0.0004231161,0.0003325694,0.7202004,0.06449397,0.03040541,0.001940694,0.178577],"study_design_scores_gemma":[0.000003608827,0.00004637846,0.0002572041,0.000004118887,0.000008827501,0.00007607449,0.00003118716,0.993276,0.004253278,0.001450711,0.0005779894,0.00001455018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02472988,0.0002370516,0.9733138,0.0001101489,0.00002973924,0.00001780916,0.00003188086,0.000295154,0.001234504],"genre_scores_gemma":[0.8240895,0.0004536548,0.1730796,0.0001221133,0.00005932082,0.0000537021,0.0001235725,0.00003541039,0.001983165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003118129,"threshold_uncertainty_score":0.006199956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03693887491084374,"score_gpt":0.2989945903976763,"score_spread":0.2620557154868325,"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."}}