{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000799716,0.0001187135,0.0001321879,0.00007802682,0.0001014835,0.00004098611,0.0006230056,0.00008658108,0.00002286194],"category_scores_gemma":[0.0001373861,0.0001176372,0.00003650509,0.0003888829,0.00003867785,0.0002255855,0.000256221,0.0001293424,0.00005280879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003545038,"about_ca_system_score_gemma":0.000003897961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004422646,"about_ca_topic_score_gemma":0.000428603,"domain_scores_codex":[0.9992598,0.000008929431,0.0001869648,0.0001362704,0.00007017926,0.0003378555],"domain_scores_gemma":[0.9989615,0.0004155666,0.000022702,0.0005363505,0.00003281977,0.00003108251],"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":[0.000005476645,0.000003125341,0.001346208,0.00001526666,0.00001262351,7.998854e-7,0.00001945861,0.9640437,0.0001562553,0.001392021,0.01407122,0.01893383],"study_design_scores_gemma":[0.0002682151,0.000008532144,0.002447011,0.000006343818,0.000003053761,7.905021e-7,0.00009559172,0.9761133,0.004412743,0.00358978,0.01289347,0.000161167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1229927,0.0005321056,0.8609692,0.0009057898,0.0004539034,0.0005937994,0.00001936153,0.01231553,0.001217571],"genre_scores_gemma":[0.960107,0.0003227697,0.03886149,0.00006723109,0.00006666907,0.000251907,0.00004994497,0.00005349341,0.0002194677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8371143,"threshold_uncertainty_score":0.4797106,"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."}}