{"id":"W3013736034","doi":"10.1109/ccnc46108.2020.9045329","title":"Deep Learning Decoder for MIMO Communications with Impulsive Noise","year":2020,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; MIMO; Noise (video); Decoding methods; 3G MIMO; Electronic engineering; Telecommunications; Artificial intelligence; Speech recognition; Engineering; Beamforming","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":[0.0007531142,0.0004736845,0.0005638984,0.0002465521,0.0002157763,0.0005545578,0.0005365287,0.0006578633,0.001308725],"category_scores_gemma":[0.002491872,0.0002324139,0.0002711518,0.0003403435,0.000569956,0.0007575636,0.0006250029,0.0008541686,0.0002639209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008583411,"about_ca_system_score_gemma":0.001330979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004268666,"about_ca_topic_score_gemma":0.005003896,"domain_scores_codex":[0.9996266,0.0001006731,0.00001756246,0.00005982292,0.0001377541,0.0000575166],"domain_scores_gemma":[0.9990466,0.000645749,0.00006222133,0.00006301279,0.0001562312,0.00002623374],"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":[0.00005310081,0.00001971955,0.0003423163,0.00004677958,0.00001789601,0.00005321599,0.00002767837,0.9542709,0.002490212,0.02177343,0.0004522073,0.02045256],"study_design_scores_gemma":[0.000002030736,0.00001113417,0.0000318223,0.000001994023,0.000001906912,0.000009260022,0.000002045061,0.996847,0.000782312,0.002165178,0.000143633,0.000001735159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02510389,0.0002773938,0.9713538,0.0001741897,0.00002855802,0.00001891834,0.00008124264,0.0002946074,0.002667427],"genre_scores_gemma":[0.8568302,0.000358689,0.136751,0.0001932191,0.00004290338,0.00005925247,0.0001860495,0.00004588446,0.005532763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004268666,"threshold_uncertainty_score":0.008487642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193850686667621,"score_gpt":0.2564804380440819,"score_spread":0.2345419311774057,"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."}}