{"id":"W7083285335","doi":"10.1109/tifs.2025.3614468","title":"Precoding Design for Key Generation in Extremely Large-Scale MIMO Near-Field Multi-User Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; Engineering and Physical Sciences Research Council; Department for the Economy; Canada Excellence Research Chairs, Government of Canada; National Natural Science Foundation of China; Queen's University Belfast; Natural Sciences and Engineering Research Council of Canada; Queen's University; European Commission","keywords":"Eavesdropping; Precoding; Artificial noise; Key generation; Randomness; Key (lock); Base station; Physical layer; Noise (video); MIMO","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":[0.0004032219,0.0001080023,0.000125058,0.00010285,0.000309264,0.0002523083,0.0001295486,0.0001249119,0.000003563314],"category_scores_gemma":[0.00002150488,0.0001075828,0.00004414848,0.0002171741,0.00001708575,0.0008144759,0.000003902303,0.0001448462,0.000002601262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003099718,"about_ca_system_score_gemma":0.00004884086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003369437,"about_ca_topic_score_gemma":0.00008042132,"domain_scores_codex":[0.9992244,0.0000323938,0.0003174238,0.000152111,0.00009262853,0.0001810971],"domain_scores_gemma":[0.9994539,0.0001029768,0.00007451438,0.0001807093,0.0001494599,0.00003844313],"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":[0.0002767726,0.0005104776,0.000402875,0.001328174,0.0001347402,0.000002678177,0.03855319,0.7089778,0.001409387,0.1387142,0.01387999,0.09580974],"study_design_scores_gemma":[0.0006567693,0.00004410378,0.00003291179,0.00003843065,0.000006141094,0.000002283708,0.000206492,0.9721947,0.007895036,0.001176981,0.0176358,0.0001103757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005136799,0.00002472677,0.9925462,0.0006646045,0.0005588274,0.0004348714,0.00001507628,0.00005443676,0.0005644379],"genre_scores_gemma":[0.9616684,0.00002887703,0.03752962,0.0003403583,0.00001451285,0.0001126664,0.0000122884,0.00000143691,0.0002917894],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9565316,"threshold_uncertainty_score":0.43871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564112114304251,"score_gpt":0.2406283717423282,"score_spread":0.2149872505992856,"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."}}