{"id":"W4388662267","doi":"10.1049/2023/6610762","title":"Preset Conditional Generative Adversarial Network for Massive MIMO Detection","year":2023,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; MIMO; Detector; Channel (broadcasting); Noise (video); SIGNAL (programming language); Detection theory; Artificial intelligence; Signal-to-noise ratio (imaging); Algorithm; Artificial neural network; Pattern recognition (psychology); Speech recognition; Telecommunications; Image (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":[],"consensus_categories":[],"category_scores_codex":[0.000382052,0.0001586947,0.0001518184,0.0001395281,0.0006154059,0.000291757,0.0003549766,0.0001102152,0.00001765669],"category_scores_gemma":[0.00006012763,0.0001632736,0.00007625884,0.0008452607,0.00005963349,0.001008472,0.00007824833,0.0001279313,0.00006132181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009958034,"about_ca_system_score_gemma":0.0001927758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003859648,"about_ca_topic_score_gemma":0.000006292889,"domain_scores_codex":[0.9983651,0.00008870299,0.000296268,0.0004949128,0.0003960391,0.0003589478],"domain_scores_gemma":[0.9989104,0.0002724358,0.0002402239,0.0001644451,0.0003281735,0.00008436968],"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":[0.0001555034,0.00007253532,0.000200925,0.000125818,0.00007145741,0.00001117898,0.001361085,0.6922075,0.09856018,0.02861335,0.01496057,0.1636599],"study_design_scores_gemma":[0.0004759533,0.00007671699,0.001552458,0.00003198383,0.00001004895,0.00000338209,0.00003842725,0.9284445,0.01495288,0.05268252,0.001535045,0.0001961035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003978152,0.00003837769,0.993338,0.001230227,0.0003786386,0.0004215755,0.00002592221,0.0004240577,0.0001650062],"genre_scores_gemma":[0.9713157,0.000001284985,0.02664353,0.0001824104,0.001214683,0.0002505552,0.0001720474,0.00001913572,0.0002006135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9673376,"threshold_uncertainty_score":0.6658103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912166990310937,"score_gpt":0.2836421454314026,"score_spread":0.2445204755282932,"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."}}