{"id":"W4291722333","doi":"10.1109/tvt.2022.3175971","title":"Secure Transmission for MISO Wiretap Channels Using General Multi-Fractional Fourier Transform: An Approach in Signal Domain","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Artificial noise; Transmitter; Transmission (telecommunications); Computer science; Secure transmission; Interference (communication); Superposition principle; Transmitter power output; SIGNAL (programming language); Noise (video); Electronic engineering; Signal-to-noise ratio (imaging); Channel (broadcasting); Telecommunications; Mathematics; Engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003916412,0.0006143095,0.0004440141,0.0003798016,0.0005258845,0.000621511,0.0004695146,0.0006835991,0.0009530645],"category_scores_gemma":[0.0006166609,0.0001669099,0.0004214831,0.0004751053,0.0008550786,0.001483867,0.0007053515,0.0008068375,0.0002484785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004158889,"about_ca_system_score_gemma":0.0003045213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004247795,"about_ca_topic_score_gemma":0.0004414252,"domain_scores_codex":[0.9996531,0.00009835223,0.00001993632,0.00005203282,0.0001347126,0.00004189759],"domain_scores_gemma":[0.9996982,0.0001078049,0.00006138683,0.00006387081,0.00005693071,0.0000118332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005699376,0.0001236261,0.001537465,0.0006187809,0.0001390467,0.001050875,0.0005895072,0.1985261,0.1949897,0.3822497,0.002958039,0.2166473],"study_design_scores_gemma":[0.00002618652,0.0002411823,0.0002441833,0.00004061638,0.00004236514,0.0006855074,0.00007697894,0.9375949,0.03435842,0.02114264,0.005495087,0.00005193646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0316903,0.001151665,0.962302,0.0003040641,0.00009295514,0.00003335905,0.00002288271,0.000135917,0.004266875],"genre_scores_gemma":[0.8271521,0.001745725,0.1673954,0.0002093132,0.0001135397,0.0000736181,0.00003857223,0.00002427723,0.003247572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009530645,"threshold_uncertainty_score":0.003188372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456390041573002,"score_gpt":0.2668105868319682,"score_spread":0.2422466864162382,"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."}}