{"id":"W3002563005","doi":"10.1109/iwsda46143.2019.8966120","title":"Deep Learning Based Modified Message Passing Algorithm for Sparse Code Multiple Access","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Message passing; Computer science; Algorithm; Code (set theory); Convergence (economics); Artificial neural network; Rate of convergence; Computational complexity theory; Graph; Factor graph; Theoretical computer science; Artificial intelligence; Parallel computing; Decoding methods; Channel (broadcasting); Telecommunications","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":[0.0001000331,0.0001588872,0.0001980426,0.0001174108,0.0000956363,0.00007111235,0.0005493548,0.0001183329,0.00005481775],"category_scores_gemma":[0.00009401677,0.0001610233,0.00005684381,0.0001725208,0.00003191342,0.000341363,0.0001229811,0.0002512985,0.00002881271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009561551,"about_ca_system_score_gemma":0.000007883804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007541135,"about_ca_topic_score_gemma":0.00002071137,"domain_scores_codex":[0.9992182,0.00001742953,0.0002095515,0.0001837204,0.00009826987,0.000272797],"domain_scores_gemma":[0.9989289,0.000396464,0.00005006491,0.0005401741,0.00005081562,0.00003354869],"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":[0.000002776737,0.000009101924,0.0003682867,0.00002993487,0.00001127969,2.84841e-7,0.00001631079,0.782041,0.002336979,0.0002612877,0.00002658022,0.2148962],"study_design_scores_gemma":[0.000685825,0.00001403089,0.0002145451,0.00002202448,0.000003804531,3.711712e-7,0.0001544869,0.9461012,0.04783065,0.0002357624,0.00452618,0.0002111335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01225781,0.0001727716,0.9828364,0.00007125714,0.0001081784,0.0003867031,0.000005478667,0.0025575,0.001603877],"genre_scores_gemma":[0.7874653,0.00004878486,0.2120635,0.00002775552,0.000009276618,0.0001159714,0.00002846673,0.00004953821,0.0001913506],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7752075,"threshold_uncertainty_score":0.6566339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082261797989211,"score_gpt":0.2733619115541054,"score_spread":0.2425392935742133,"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."}}