{"id":"W2314565273","doi":"10.1109/twc.2016.2535442","title":"Physical Layer Authentication Enhancement Using Two-Dimensional Channel Quantization","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; False alarm; Quantization (signal processing); Physical layer; Algorithm; MIMO; Multipath propagation; Channel (broadcasting); Wireless; Test statistic; Spoofing attack; Statistical hypothesis testing; Mathematics; Statistics; Artificial intelligence; Computer network; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000205746,0.0002958192,0.0002813293,0.0003171242,0.0005003018,0.00005012889,0.0009844664,0.000118338,0.00007204719],"category_scores_gemma":[0.00000603722,0.0002707964,0.0001486226,0.0004967765,0.0002478675,0.0004521436,0.00001808118,0.0003405944,0.0001755248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000345961,"about_ca_system_score_gemma":0.00004841595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004034434,"about_ca_topic_score_gemma":0.00008427265,"domain_scores_codex":[0.9983653,0.0002042295,0.0004779774,0.0002829549,0.0003499133,0.0003195714],"domain_scores_gemma":[0.9966647,0.0003833998,0.0001114532,0.00250325,0.0002178693,0.0001193153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003660199,0.0009703487,0.00001223384,0.0000370092,0.000178957,5.426056e-7,0.001182314,0.05872681,0.8823819,0.008703183,0.000186657,0.04758343],"study_design_scores_gemma":[0.0005190501,0.00004629285,0.00002845832,0.0002027606,0.00005774984,0.000005784651,0.00003350828,0.5190367,0.4783182,0.00104435,0.0003392095,0.0003678826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.255188,0.00007870824,0.7419382,0.0008979479,0.0001980324,0.0003964022,0.00005685632,0.0009107059,0.0003351348],"genre_scores_gemma":[0.9905809,0.0004534876,0.008315641,0.00006953068,0.00003669978,0.0003412514,0.00002501293,0.00008568063,0.00009183143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7353928,"threshold_uncertainty_score":0.9999744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04496404008474349,"score_gpt":0.3044346510291962,"score_spread":0.2594706109444527,"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."}}