{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004907479,0.0005479961,0.0003890739,0.0002178508,0.0002720604,0.0005691425,0.0004945099,0.0005582479,0.001166884],"category_scores_gemma":[0.001454431,0.0002510553,0.0002550891,0.0003744192,0.0005644315,0.0007848596,0.0006238947,0.0005388288,0.000446027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004244429,"about_ca_system_score_gemma":0.0005645918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005645598,"about_ca_topic_score_gemma":0.0007012349,"domain_scores_codex":[0.9995021,0.0001757415,0.00002367243,0.00008526841,0.0001702649,0.0000429209],"domain_scores_gemma":[0.9995222,0.0002066967,0.00008391789,0.0000508184,0.0001176264,0.00001874718],"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":[0.0001174307,0.00005242989,0.0005320277,0.0002336358,0.00004958654,0.0001749175,0.0001956172,0.7768097,0.04591861,0.0739498,0.001513532,0.1004527],"study_design_scores_gemma":[0.000009968008,0.00007774032,0.00009706416,0.0000101363,0.000006611715,0.00006043099,0.00001831807,0.9894293,0.003616192,0.005263764,0.001399311,0.00001119015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005567988,0.0001431818,0.9928773,0.00005788701,0.00001910907,0.00001882473,0.00001098353,0.00004232935,0.00126238],"genre_scores_gemma":[0.6759713,0.0007568926,0.3194274,0.0001424069,0.00007859602,0.0001999489,0.0000746413,0.00003494799,0.003313933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001166884,"threshold_uncertainty_score":0.003903627,"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."}}