{"id":"W3172135703","doi":"10.1109/jsac.2021.3087250","title":"Radio Frequency Fingerprint Identification for LoRa Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":259,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Royal Society","keywords":"Computer science; Deep learning; Spectrogram; Artificial intelligence; Universal Software Radio Peripheral; Carrier frequency offset; Multilayer perceptron; Convolutional neural network; Wireless; Time–frequency analysis; Artificial neural network; Pattern recognition (psychology); Speech recognition; Machine learning; Frequency offset; Orthogonal frequency-division multiplexing; Filter (signal processing); Telecommunications; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004369042,0.0007319747,0.000544255,0.0006897897,0.0002366256,0.0005555024,0.0007571432,0.0006339988,0.001752126],"category_scores_gemma":[0.001135427,0.0002156881,0.0003974157,0.0005241103,0.0002618803,0.0009480326,0.0009390093,0.0007437755,0.00106769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004942137,"about_ca_system_score_gemma":0.0004694285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002348434,"about_ca_topic_score_gemma":0.003186396,"domain_scores_codex":[0.9996848,0.00004459854,0.00001566314,0.00007627615,0.0001215187,0.00005712406],"domain_scores_gemma":[0.9996669,0.00007891884,0.00007058996,0.00005533018,0.0001087662,0.00001949626],"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":[0.0002149805,0.0001755674,0.005959701,0.0001491682,0.00008391311,0.0001644914,0.00006456719,0.1071433,0.03665595,0.002367907,0.004053622,0.8429668],"study_design_scores_gemma":[0.000006799923,0.00009771716,0.001862574,0.00001935271,0.00002067591,0.0001130421,0.00002400227,0.9811068,0.01259273,0.001557301,0.002583724,0.00001537916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07278154,0.001286905,0.9174012,0.0003554406,0.00010929,0.00006585697,0.0002461722,0.003508677,0.004244911],"genre_scores_gemma":[0.8566751,0.0006499038,0.1347758,0.000303478,0.00005375474,0.00008176008,0.0005624277,0.00005663723,0.006841165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002348434,"threshold_uncertainty_score":0.005861461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06964920209736099,"score_gpt":0.3229304058749288,"score_spread":0.2532812037775678,"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."}}