{"id":"W3042219597","doi":"10.1109/tvt.2021.3109236","title":"Spectrum Sensing and Signal Identification With Deep Learning Based on Spectral Correlation Function","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Vehicular Technology","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Qatar National Research Fund; European Commission; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Cyclostationary process; Computer science; SIGNAL (programming language); A priori and a posteriori; Wireless; Identification (biology); Artificial intelligence; Convolutional neural network; Correlation; Process (computing); Pattern recognition (psychology); Function (biology); Correlation function (quantum field theory); Property (philosophy); Spectrum (functional analysis); Spectral density; Telecommunications; Channel (broadcasting); Mathematics","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":[0.0004132019,0.000716749,0.0004944467,0.000517981,0.0002580618,0.0005160433,0.0007929258,0.0007405631,0.001802221],"category_scores_gemma":[0.0009935285,0.0002809707,0.0005233303,0.0005295774,0.000396517,0.001081913,0.0008147481,0.0009293561,0.0004969264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005578141,"about_ca_system_score_gemma":0.0006987383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005818473,"about_ca_topic_score_gemma":0.006555933,"domain_scores_codex":[0.9997756,0.00003557745,0.0000105696,0.00007080145,0.00006509759,0.00004233501],"domain_scores_gemma":[0.9997787,0.00008083221,0.00002785997,0.00003909875,0.00005796912,0.00001553308],"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":[0.0002212359,0.00016575,0.001971276,0.0001024338,0.00009086633,0.0001485928,0.00006902812,0.5073135,0.0273574,0.01576995,0.003189527,0.4436004],"study_design_scores_gemma":[0.000001169304,0.000009138724,0.00009853336,0.000001848345,0.000002959401,0.000008981473,0.000002534346,0.9967052,0.001812869,0.00112828,0.0002261773,0.000002254167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03153432,0.0003311207,0.9647686,0.0002060853,0.00005174741,0.00002083305,0.00007949209,0.0009060175,0.002101757],"genre_scores_gemma":[0.7508526,0.0005182188,0.2401143,0.0002337055,0.00007406122,0.00006799456,0.0005343987,0.00008516594,0.007519525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005818473,"threshold_uncertainty_score":0.01156926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00964164372311976,"score_gpt":0.207939283915645,"score_spread":0.1982976401925252,"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."}}