{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003047878,0.00043092,0.0003766949,0.001303924,0.0004883799,0.0003770932,0.0003569419,0.0008036098,0.00002202591],"category_scores_gemma":[0.0000141175,0.0004615155,0.0001263302,0.001051593,0.0001634109,0.0002945528,0.00001327145,0.001970189,0.00002690663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000366282,"about_ca_system_score_gemma":0.000164737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000250026,"about_ca_topic_score_gemma":0.0001133995,"domain_scores_codex":[0.9969819,0.000255856,0.0004977969,0.001345822,0.0005714087,0.0003471888],"domain_scores_gemma":[0.9980605,0.0001386537,0.0004586472,0.001008301,0.0002493633,0.00008450069],"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":[0.0000561589,0.0001290615,0.00009435711,0.00004022559,0.00006496716,0.00002647743,0.00008355463,0.9231135,0.008611488,0.000964588,0.000001152978,0.06681447],"study_design_scores_gemma":[0.0004694211,0.0003800024,0.002729066,0.0002202483,0.0001001577,0.00005553342,0.00008153454,0.9421376,0.0518497,0.001531976,0.00002279529,0.0004219836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09266143,0.00005865776,0.9027458,0.002414432,0.0006176546,0.0005074844,0.000002681058,0.0009529927,0.00003881401],"genre_scores_gemma":[0.9857204,0.00002277165,0.0138939,0.0000841258,0.00004976467,0.00007029498,0.0000497564,0.00004963161,0.0000593301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.893059,"threshold_uncertainty_score":0.9997836,"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."}}