{"id":"W4376480760","doi":"10.1109/wcnc55385.2023.10118714","title":"Deep Residual Neural Network Decoder for Sparse Code Multiple Access","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Soft-decision decoder; Residual; Rayleigh fading; Artificial neural network; Robustness (evolution); Bit error rate; Fading; Algorithm; Message passing; Additive white Gaussian noise; Computational complexity theory; Computer engineering; Artificial intelligence; Parallel computing; Computer network; Channel (broadcasting)","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":[0.0004522223,0.0003906996,0.0003643324,0.0002241803,0.0001590702,0.0003553832,0.0006676504,0.0005676188,0.001796402],"category_scores_gemma":[0.00153419,0.0001593715,0.0002084452,0.0002497385,0.0003277949,0.0005205565,0.0004808066,0.000976003,0.0004567727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004323919,"about_ca_system_score_gemma":0.0009623707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004287418,"about_ca_topic_score_gemma":0.007575646,"domain_scores_codex":[0.9997962,0.00005163538,0.00001139909,0.00003544792,0.00007948129,0.00002576907],"domain_scores_gemma":[0.9995965,0.000183262,0.00003854548,0.00003918808,0.0001267151,0.00001582859],"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":[0.0002288004,0.00009137794,0.0008243615,0.0001317695,0.00006263363,0.0001334133,0.00006684454,0.7407787,0.02515104,0.02721221,0.003860252,0.2014587],"study_design_scores_gemma":[0.000003172647,0.00001781787,0.00003458421,0.000002433838,0.00000274121,0.00001295423,0.000001578861,0.9957193,0.00255552,0.00129891,0.0003487915,0.000002342051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02538835,0.0004315382,0.9697576,0.000244303,0.00005912786,0.00002837348,0.000140308,0.0008099818,0.003140371],"genre_scores_gemma":[0.7201336,0.000413333,0.2689791,0.0002884437,0.00006171623,0.00009719469,0.0005457992,0.00009315451,0.009387677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004287418,"threshold_uncertainty_score":0.008524954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06334486397509352,"score_gpt":0.312010675683885,"score_spread":0.2486658117087915,"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."}}