{"id":"W3205407875","doi":"10.1109/tvt.2022.3164902","title":"An Ultra-Reliable Low-Latency Non-Binary Polar Coded SCMA Scheme","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Decoding methods; Computer science; Bit error rate; Latency (audio); Polar code; Message passing; Algorithm; Binary number; Transmission (telecommunications); Computer engineering; Real-time computing; Theoretical computer science; Parallel computing; Arithmetic; Telecommunications; 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":[0.0003492415,0.0005191653,0.0003884246,0.0004184483,0.0005014192,0.0006390172,0.0008286798,0.0004627513,0.001419971],"category_scores_gemma":[0.001091452,0.0002217832,0.0002796303,0.0006271973,0.0005570063,0.000875474,0.0008558342,0.0007072425,0.000445499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004535833,"about_ca_system_score_gemma":0.001070146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506367,"about_ca_topic_score_gemma":0.001842433,"domain_scores_codex":[0.9994949,0.0001215768,0.00002357378,0.00007657809,0.0002265848,0.00005668572],"domain_scores_gemma":[0.9994388,0.0001562901,0.00008105843,0.00009059139,0.000196934,0.0000363757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005785007,0.0001585004,0.001331923,0.0004563883,0.00008242812,0.0007729764,0.0005723095,0.2747847,0.2607042,0.1764385,0.005540371,0.2785793],"study_design_scores_gemma":[0.00004330715,0.0001727192,0.0001570534,0.00001725237,0.00002454022,0.0002913621,0.00003142842,0.9668477,0.01944648,0.0083364,0.004599866,0.00003191762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02657139,0.0003973405,0.9670397,0.0002905021,0.00007264568,0.00005666939,0.00005176752,0.0003242615,0.005195722],"genre_scores_gemma":[0.78597,0.000693401,0.2061217,0.0003228007,0.00007583472,0.0001059769,0.0001156296,0.00002919952,0.006565436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001506367,"threshold_uncertainty_score":0.004750252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006850880547948014,"score_gpt":0.2222869874819116,"score_spread":0.2154361069339635,"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."}}