{"id":"W2611515111","doi":"10.1109/tvt.2017.2700475","title":"Artificial Noise Assisted Secure Interference Networks With Wireless Power Transfer","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"National Natural Science Foundation of China","keywords":"Eavesdropping; Interference (communication); Artificial noise; Computer science; Transmitter power output; Transmitter; Wireless; Noise (video); Electronic engineering; Energy (signal processing); Signal-to-noise ratio (imaging); Power (physics); Zero-forcing precoding; Telecommunications; Computer network; Engineering; Channel (broadcasting); Precoding; MIMO; Mathematics; Artificial intelligence; Physics","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":[0.0007695635,0.0009208856,0.0007026952,0.0004660295,0.0005666448,0.0008675349,0.001005308,0.0006413229,0.0009953115],"category_scores_gemma":[0.001441582,0.0002303286,0.000422019,0.0009462026,0.001022621,0.0013729,0.001507911,0.0007567443,0.0003004954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000717149,"about_ca_system_score_gemma":0.0004597483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004501585,"about_ca_topic_score_gemma":0.0005432511,"domain_scores_codex":[0.9992189,0.0002884225,0.00003607862,0.0001164941,0.0002365934,0.0001034885],"domain_scores_gemma":[0.9992433,0.0004233245,0.0001382447,0.00006526396,0.0001017931,0.00002801635],"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":[0.000501958,0.0001353234,0.0009012512,0.0002753403,0.0001085734,0.0005734364,0.0002529486,0.7267329,0.03839634,0.09741077,0.001708345,0.1330028],"study_design_scores_gemma":[0.00001807277,0.0001044509,0.00008357791,0.00001264854,0.00002440707,0.0001259646,0.00002445394,0.9784425,0.005392785,0.01440395,0.00135171,0.00001543336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02315561,0.0005795723,0.9708109,0.0001754048,0.00005202385,0.00003740703,0.00002234164,0.0001955248,0.004971266],"genre_scores_gemma":[0.9241673,0.0007133607,0.07160089,0.0001729558,0.000060026,0.0001219075,0.00003639949,0.00002831337,0.003098776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001005308,"threshold_uncertainty_score":0.005203307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043689431162866,"score_gpt":0.2100921716631096,"score_spread":0.1996552773514809,"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."}}