{"id":"W4255552756","doi":"10.36227/techrxiv.12671099","title":"Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Interference (communication); Monte Carlo method; Subspace topology; Likelihood-ratio test; Electromagnetic interference; Physics; Electronic engineering; Eigenvalues and eigenvectors; Wireless; Test statistic; MIMO; Computer science; Channel (broadcasting); Algorithm; Telecommunications; Mathematics; Engineering; Statistics; Statistical hypothesis testing; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001370432,0.000511794,0.0007174699,0.000529664,0.0002584469,0.0007264498,0.0006041687,0.0006641267,0.002534036],"category_scores_gemma":[0.004196324,0.0002620304,0.000462784,0.0005534859,0.0008005431,0.0008643529,0.0009427507,0.0008614062,0.001045303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006051109,"about_ca_system_score_gemma":0.001038967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007167492,"about_ca_topic_score_gemma":0.0009292544,"domain_scores_codex":[0.9987991,0.0005068126,0.00003845274,0.0001339356,0.0004491332,0.00007255662],"domain_scores_gemma":[0.9985769,0.0007890955,0.0001399017,0.0001692809,0.0002669925,0.00005787211],"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":[0.000589538,0.0001854531,0.002057758,0.0002348912,0.0001500734,0.0002032195,0.0001581129,0.5812559,0.06703117,0.09408557,0.003647728,0.2504005],"study_design_scores_gemma":[0.000007123548,0.00005539253,0.0002061819,0.000004959285,0.000004554847,0.00005543707,0.000005816178,0.9870833,0.005733982,0.006236038,0.0005942966,0.0000129406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007482995,0.0001115244,0.9910779,0.00007665104,0.00002261224,0.00002021423,0.00002623586,0.0002717171,0.000910179],"genre_scores_gemma":[0.4589525,0.0002681822,0.5357863,0.0001832723,0.00007144399,0.0001004649,0.0002100986,0.00009540534,0.00433232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002534036,"threshold_uncertainty_score":0.008477151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043824756642731,"score_gpt":0.2911387893431586,"score_spread":0.1867563136788856,"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."}}