{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001461488,0.001495447,0.0009885874,0.0004460551,0.0003000775,0.0006287657,0.001298202,0.001118639,0.001719938],"category_scores_gemma":[0.003733038,0.0005741078,0.0006818362,0.0003982672,0.001265156,0.001071955,0.001519334,0.002454304,0.000404506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008662534,"about_ca_system_score_gemma":0.0006172775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050779,"about_ca_topic_score_gemma":0.003446954,"domain_scores_codex":[0.9992105,0.0003126789,0.0000289815,0.0002025186,0.000158467,0.00008697482],"domain_scores_gemma":[0.9978698,0.001587589,0.0001363835,0.0001499744,0.0001927381,0.00006342834],"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":[0.00009048967,0.00003429354,0.0007648899,0.00006048082,0.0000480296,0.00007658044,0.00003785981,0.9464021,0.002061789,0.01017928,0.001538988,0.03870529],"study_design_scores_gemma":[0.000001656444,0.000009541561,0.0000509964,0.000002691462,0.000003104194,0.00001079357,0.000001895517,0.9971879,0.0004463373,0.002143061,0.0001388805,0.000003159023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01118339,0.0003879937,0.9862053,0.0002558707,0.00004813769,0.00002779131,0.00007591489,0.0004258239,0.001389765],"genre_scores_gemma":[0.8452396,0.0006522096,0.1465983,0.0007167846,0.0001047796,0.0001484156,0.0005020201,0.0001512203,0.005886764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003050779,"threshold_uncertainty_score":0.007729173,"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."}}