{"id":"W4400878491","doi":"10.1109/jproc.2024.3420127","title":"Safeguarding Next-Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial","year":2024,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Advanced Authentication Protocols Security","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Key Research and Development Projects of Shaanxi Province; National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Safeguarding; Computer science; Layer (electronics); Physical security; Computer security; Materials science; Nanotechnology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003305699,0.0001552039,0.0001704744,0.00008859523,0.0001697869,0.0006304379,0.001295213,0.00006642702,0.000001956378],"category_scores_gemma":[0.0001431646,0.0001191338,0.0001312086,0.0006699211,0.0000590042,0.002758955,0.0004516612,0.0002387068,0.000003978766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001211129,"about_ca_system_score_gemma":0.00007239512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001341412,"about_ca_topic_score_gemma":0.000001354615,"domain_scores_codex":[0.9986791,0.0000129746,0.0002578848,0.0004236862,0.0004111992,0.0002151512],"domain_scores_gemma":[0.9992734,0.00005941816,0.000160475,0.0002183582,0.0002425433,0.00004585162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007094817,0.00006971523,0.0002107548,0.0001730758,0.00001628122,3.837555e-7,0.002150961,0.00004285429,0.893583,0.1007328,0.0002632354,0.002749788],"study_design_scores_gemma":[0.00006957655,0.00001422179,0.00001343482,0.0001115877,0.00001027698,0.000005718392,0.00001140787,0.3290101,0.6074926,0.06243915,0.0007180583,0.0001038832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7361428,0.00004525345,0.2556252,0.0006753583,0.002107042,0.004128381,0.000007281484,0.0007051218,0.000563631],"genre_scores_gemma":[0.9867365,0.000001478124,0.01174966,0.00003894434,0.001061209,0.000374014,2.744363e-7,0.0000167713,0.00002108544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3289673,"threshold_uncertainty_score":0.6079327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08615646027087098,"score_gpt":0.3486303385804478,"score_spread":0.2624738783095768,"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."}}