{"id":"W2735673061","doi":"10.1109/i2mtc.2017.7969734","title":"Fast and robust identification of GSM and LTE signals","year":2017,"lang":"en","type":"article","venue":"","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Allen-Vanguard (Canada); Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GSM; Cognitive radio; Computer science; Frequency offset; Software-defined radio; Identification (biology); Algorithm; Real-time computing; Offset (computer science); Key (lock); SIGNAL (programming language); Electronic engineering; Orthogonal frequency-division multiplexing; Telecommunications; Engineering; Wireless","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":[0.0004738529,0.0005980053,0.000348516,0.001046288,0.0002910788,0.0005312202,0.0003148498,0.0006227978,0.0007536357],"category_scores_gemma":[0.00253491,0.0001033603,0.0002475591,0.0004958163,0.0002725506,0.0005360144,0.0005253096,0.0003738221,0.0007520209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147196,"about_ca_system_score_gemma":0.0003218511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007989617,"about_ca_topic_score_gemma":0.000750458,"domain_scores_codex":[0.9994553,0.00009378631,0.00002992757,0.0001005841,0.0002418696,0.00007853526],"domain_scores_gemma":[0.9995171,0.0001725873,0.00008233971,0.00007175704,0.0001402353,0.00001599292],"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":[0.0004799863,0.0000746741,0.00557779,0.00007818162,0.00004336081,0.0001883418,0.0001110349,0.04687769,0.1425122,0.004744672,0.001662807,0.7976493],"study_design_scores_gemma":[0.00002931396,0.0003174866,0.01729009,0.00003400096,0.00003879656,0.001175957,0.0001057077,0.8040468,0.1622021,0.005307606,0.009381657,0.0000705833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09433902,0.0003789497,0.901563,0.00007184541,0.00007397843,0.00003820192,0.00008795501,0.001299332,0.002147652],"genre_scores_gemma":[0.8457997,0.0002111576,0.1505823,0.00008509987,0.00006107888,0.00005588602,0.0002824924,0.00008573845,0.00283654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001046288,"threshold_uncertainty_score":0.002521157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984897875114152,"score_gpt":0.2682194528839272,"score_spread":0.2283704741327857,"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."}}