{"id":"W3094666940","doi":"10.1109/lcomm.2020.3034956","title":"Low-Complexity SCMA Detection for Unsupervised User Access","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"Engineering and Physical Sciences Research Council; Huawei Technologies","keywords":"Computer science; Base station; Computer network; Transmission (telecommunications); Latency (audio); Multiuser detection; Noma; Rendering (computer graphics); Telecommunications link; Telecommunications; Artificial intelligence; Code division multiple access","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":[0.00007530292,0.0001774329,0.0001883927,0.0001056254,0.000302442,0.00008448598,0.002894894,0.00008685753,0.00001157369],"category_scores_gemma":[0.00009735568,0.0002091438,0.00008664909,0.0004741145,0.0002624513,0.0004747965,0.0003265509,0.0003629722,0.00004050395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001030258,"about_ca_system_score_gemma":0.000008799526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008780154,"about_ca_topic_score_gemma":0.00005175583,"domain_scores_codex":[0.9991307,0.00005364992,0.0003062566,0.000182728,0.00009706633,0.0002296313],"domain_scores_gemma":[0.9972547,0.0002444737,0.00006358123,0.002298305,0.00007207262,0.00006686201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003146631,0.0000814999,0.0002617605,0.0002565554,0.0001067022,5.415927e-7,0.000695181,0.1186724,0.7977507,0.002683289,0.008336104,0.07112383],"study_design_scores_gemma":[0.001200429,0.00004611623,0.001372974,0.00006458539,0.0000367331,0.000003001853,0.0002953408,0.3430651,0.5718327,0.001613197,0.07964379,0.0008259754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09182441,0.0003164746,0.8797408,0.02446127,0.0001451797,0.000625996,0.00004099484,0.002598852,0.0002459722],"genre_scores_gemma":[0.9524114,0.0004651056,0.04447039,0.002037022,0.00003979975,0.0004653152,0.00005018658,0.00005721625,0.000003594875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8605869,"threshold_uncertainty_score":0.8528637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1032598778869415,"score_gpt":0.3033453411866932,"score_spread":0.2000854632997517,"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."}}