{"id":"W2946618393","doi":"10.1109/twc.2019.2916363","title":"Privacy Preservation via Beamforming for NOMA","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Computer science; Beamforming; Noma; Single antenna interference cancellation; Quality of service; Transmission (telecommunications); Computer network; Artificial noise; Mathematical optimization; Telecommunications; Telecommunications link; Mathematics; Transmitter; Channel (broadcasting)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001363212,0.0002491308,0.0002631641,0.0002894193,0.0004179267,0.00004616342,0.001832718,0.0001863971,0.00004251143],"category_scores_gemma":[0.000009672532,0.0002878142,0.0001438498,0.0004998968,0.0001261155,0.0007129796,0.00001803762,0.0005190168,0.0001456858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001993543,"about_ca_system_score_gemma":0.00002694355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001243337,"about_ca_topic_score_gemma":0.00004968743,"domain_scores_codex":[0.9987947,0.00004228373,0.0004516852,0.0002307985,0.0001645433,0.0003159918],"domain_scores_gemma":[0.9954468,0.0005997589,0.00009486105,0.003653524,0.0001455301,0.00005951085],"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":[0.00003446509,0.000324845,0.00006250746,0.0001631734,0.0001587113,1.221957e-7,0.0006658872,0.6481003,0.05359365,0.007610252,0.0001827989,0.2891033],"study_design_scores_gemma":[0.0008134149,0.0001006881,0.00008218497,0.0001065721,0.00002906205,0.000004616154,0.0002494986,0.8673331,0.1040419,0.00222996,0.0245079,0.0005011362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03950012,0.0002017489,0.9552339,0.0007500654,0.0002853774,0.001101735,0.00006188559,0.001799319,0.001065868],"genre_scores_gemma":[0.9330275,0.001074622,0.06421939,0.00005705838,0.00001065026,0.001144209,0.00005318477,0.00008690068,0.0003264839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8935274,"threshold_uncertainty_score":0.9999574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746097866124859,"score_gpt":0.2661426336798034,"score_spread":0.2386816550185548,"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."}}