{"id":"W2883214511","doi":"10.1109/tim.2018.2849478","title":"A Robust Modulation Classification Method for PSK Signals Using Random Graphs","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":31,"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":"Phase-shift keying; Computer science; Modulation (music); Quadrature amplitude modulation; Amplitude and phase-shift keying; Algorithm; Fading; Pattern recognition (psychology); Keying; Fourier transform; Speech recognition; Electronic engineering; Artificial intelligence; Mathematics; Telecommunications; Bit error rate; Acoustics; Physics; Engineering; Decoding methods","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101577,0.0001992628,0.0001817055,0.0003650216,0.0005958437,0.0001900465,0.0001785449,0.0000952702,0.0000211489],"category_scores_gemma":[0.00001556087,0.000205064,0.00009526552,0.0004509972,0.0000652589,0.0007173656,0.00000172824,0.00008878966,0.00000922277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002928539,"about_ca_system_score_gemma":0.0001005764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000036276,"about_ca_topic_score_gemma":0.00004136642,"domain_scores_codex":[0.997905,0.0002023166,0.000481903,0.0005464969,0.0006449413,0.0002194042],"domain_scores_gemma":[0.9985538,0.00009456184,0.0002569275,0.0003240441,0.0006558453,0.0001148876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002978183,0.0002796132,0.00005000219,0.00004710621,0.0001129107,1.487908e-7,0.001131022,0.05076547,0.4761284,0.01271638,0.00006212136,0.458409],"study_design_scores_gemma":[0.002183899,0.0001985451,0.001326201,0.00003704377,0.00004694597,0.000004763318,0.0001179651,0.8729064,0.1200268,0.002821689,0.0001276926,0.0002020599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01776499,0.00001021721,0.9795074,0.000706048,0.0007246432,0.001047825,0.00001022476,0.0001461123,0.00008252152],"genre_scores_gemma":[0.7995416,0.00001005502,0.1999345,0.0002109484,0.00005156399,0.0002094981,0.000005387608,0.00001503332,0.00002143594],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8221409,"threshold_uncertainty_score":0.8362266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1779164781393697,"score_gpt":0.3269664175089737,"score_spread":0.149049939369604,"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."}}