{"id":"W3003847901","doi":"10.1109/globalsip45357.2019.8969440","title":"A Tensor-Based Spectrum Sensing Technique for MIMO Cognitive Radio Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Cognitive radio; MIMO; Detector; Fading; Computer science; Likelihood-ratio test; Signal-to-noise ratio (imaging); Algorithm; Tensor (intrinsic definition); Monte Carlo method; Spectrum (functional analysis); Electronic engineering; Mathematics; Telecommunications; Physics; Wireless; Engineering; Decoding methods; Statistics; 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":[0.0004630375,0.000305285,0.0003889313,0.0001919954,0.00020017,0.000261176,0.0003255328,0.0001517243,0.00004353163],"category_scores_gemma":[0.00005201638,0.0002818246,0.0002352954,0.0005765126,0.00006128501,0.0002725649,0.00009890077,0.0002721138,0.00003993389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009098894,"about_ca_system_score_gemma":0.0001066401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003278225,"about_ca_topic_score_gemma":0.00003501627,"domain_scores_codex":[0.9978357,0.00009477876,0.0003081834,0.0007776947,0.0002263978,0.0007572582],"domain_scores_gemma":[0.9982008,0.0008764023,0.0001280209,0.000466504,0.0001814484,0.0001467751],"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":[0.001204268,0.0008160048,0.00905038,0.0002950998,0.0007837573,0.0005152651,0.0008731658,0.05588447,0.01738065,0.3108602,0.01249971,0.589837],"study_design_scores_gemma":[0.001235424,0.0003260076,0.0008109578,0.0002181123,0.00002353166,0.0001051471,0.00003620837,0.978225,0.01583367,0.001737949,0.0009213822,0.0005266087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004995498,0.0001616666,0.9836086,0.001117746,0.00048203,0.001598486,0.000003693327,0.000407486,0.007624814],"genre_scores_gemma":[0.8990686,0.000009693868,0.09867001,0.001455544,0.0003139857,0.00001658898,0.0000104264,0.00003764005,0.0004175009],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9223405,"threshold_uncertainty_score":0.9999634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150022091923049,"score_gpt":0.2339345863171012,"score_spread":0.2224343653978708,"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."}}