{"id":"W3088483589","doi":"10.1109/lcomm.2020.3025903","title":"Physical Layer Security Over Mixture Gamma Distributed Fading Channels With Discrete Inputs: A Unified and General Analytical Framework","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Fading; Computer science; Physical layer; Secrecy; Gaussian; Signal-to-noise ratio (imaging); Algorithm; Channel (broadcasting); Applied mathematics; Topology (electrical circuits); Mathematics; Telecommunications; Wireless","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":[0.00009937814,0.0002900018,0.0003484909,0.00008388022,0.0002083143,0.0001499753,0.001172639,0.0001431394,0.000008906361],"category_scores_gemma":[0.000050255,0.0002825693,0.00007901631,0.0006042413,0.0003497947,0.0002830714,0.0003390627,0.00108533,0.000008221878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000832027,"about_ca_system_score_gemma":0.00001366091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002131951,"about_ca_topic_score_gemma":0.00001452466,"domain_scores_codex":[0.9987425,0.0001782118,0.0002838188,0.0002666546,0.0002210883,0.0003076812],"domain_scores_gemma":[0.9975747,0.0002928163,0.00006978464,0.001792031,0.00005963977,0.0002110089],"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":[0.0005901633,0.001436436,0.01987152,0.001517544,0.00401993,0.0001404716,0.1230154,0.1153474,0.3786875,0.208926,0.1407093,0.00573821],"study_design_scores_gemma":[0.0007381692,0.00008692397,0.001455353,0.0002613584,0.0001524281,0.00001596227,0.0001538399,0.9555705,0.01207583,0.001535876,0.02690263,0.001051111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8038106,0.0002665124,0.1555416,0.03847522,0.00004421865,0.0003822756,0.0001370918,0.001085471,0.000256981],"genre_scores_gemma":[0.9857268,0.000235017,0.01093052,0.002648918,0.0001385776,0.00009405953,0.0001635144,0.00006081586,0.000001788984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8402231,"threshold_uncertainty_score":0.9999626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302687941523811,"score_gpt":0.2733684997219714,"score_spread":0.2503416203067333,"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."}}