{"id":"W3101985270","doi":"10.1109/twc.2015.2500578","title":"Linear Precoding of Data and Artificial Noise in Secure Massive MIMO Systems","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":177,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Precoding; Artificial noise; Telecommunications link; Base station; MIMO; Data transmission; Zero-forcing precoding; Polynomial; Ergodic theory","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":[0.0007439435,0.0005907194,0.0004847322,0.000202699,0.0003279606,0.0007817426,0.0004517245,0.0007175896,0.0006445067],"category_scores_gemma":[0.002073517,0.0002563063,0.0002942263,0.0004845848,0.001260481,0.0009248327,0.0007218451,0.0006107535,0.0001831531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680233,"about_ca_system_score_gemma":0.0005776776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793374,"about_ca_topic_score_gemma":0.001529284,"domain_scores_codex":[0.9992926,0.0002788091,0.00002387594,0.00009777342,0.0002162428,0.00009077877],"domain_scores_gemma":[0.9987658,0.0007313885,0.0002031488,0.0001088781,0.0001612325,0.00002958439],"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":[0.00007574995,0.000018643,0.0004780967,0.00008156514,0.00001953239,0.0002556205,0.00008747433,0.9255359,0.005872352,0.05954318,0.0002569102,0.007775022],"study_design_scores_gemma":[0.000006561191,0.00003663079,0.00009167944,0.000005373187,0.000005513455,0.00004284967,0.00001880316,0.9920204,0.001272765,0.006245487,0.0002475456,0.000006478273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07790021,0.0005750889,0.9168454,0.0002488795,0.00005288924,0.00002382041,0.00005861176,0.00008765342,0.004207449],"genre_scores_gemma":[0.9646742,0.0005524217,0.03259966,0.0000737005,0.00005122324,0.00003382419,0.00003266599,0.00001145718,0.001970964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001793374,"threshold_uncertainty_score":0.004846811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09114750185726761,"score_gpt":0.3124666539971344,"score_spread":0.2213191521398668,"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."}}