{"id":"W2751804009","doi":"10.1109/tsp.2017.2745454","title":"On Linear Precoding for the Two-User MISO Broadcast Channel With Confidential Messages and Per-Antenna Constraints","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Transmitter; Computer science; Secure transmission; Maximization; Channel (broadcasting); Optimization problem; Mathematical optimization; Artificial noise; Secrecy; Transmission (telecommunications); Multi-user; Zero-forcing precoding; Antenna (radio); MIMO; Algorithm; Computer network; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002132807,0.0002086956,0.0001891407,0.00009168532,0.001250618,0.0003807354,0.0003709603,0.00008071164,0.0000355804],"category_scores_gemma":[0.000006955303,0.000162828,0.00005672412,0.00004579481,0.0003533158,0.0003718571,0.000002983351,0.0003793399,0.00000370713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003044867,"about_ca_system_score_gemma":0.00003025718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001618236,"about_ca_topic_score_gemma":0.00004114766,"domain_scores_codex":[0.9991611,0.00002414686,0.0001984618,0.0002153989,0.000176192,0.0002246774],"domain_scores_gemma":[0.9990716,0.0002708666,0.00009471887,0.0003896563,0.0001117529,0.00006142366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006793959,0.0003082204,0.000025355,0.0008116054,0.0005139193,0.00000960172,0.006935396,0.07908509,0.06142965,0.0005990242,0.0001754225,0.8494273],"study_design_scores_gemma":[0.00221933,0.000278979,0.00007302225,0.00127055,0.0002053014,0.00008635715,0.001130786,0.7770829,0.215744,0.0003893272,0.0008355777,0.0006838472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02018927,0.0002353576,0.9778666,0.0003598613,0.00008793551,0.0004464094,0.00003193108,0.0003214856,0.0004611017],"genre_scores_gemma":[0.996823,0.0001335454,0.002633973,0.00005824087,0.00005464341,0.0001577976,0.00000151245,0.00005389137,0.00008337579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766337,"threshold_uncertainty_score":0.9618872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705610301947779,"score_gpt":0.2831523528942052,"score_spread":0.2560962498747274,"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."}}