{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005912176,0.0007291624,0.0005287346,0.0004684068,0.0005283057,0.0007745945,0.0007340987,0.0007026317,0.001119305],"category_scores_gemma":[0.003233591,0.0002716036,0.0003244222,0.0005270738,0.0007359238,0.0007252739,0.00106613,0.001171959,0.0005433855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005530174,"about_ca_system_score_gemma":0.001275403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008918743,"about_ca_topic_score_gemma":0.001896592,"domain_scores_codex":[0.999127,0.0002312772,0.00002478925,0.0001125487,0.0004225064,0.00008196011],"domain_scores_gemma":[0.9981663,0.001048765,0.0001608167,0.0002864263,0.0002822935,0.00005549446],"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":[0.0003388663,0.000238744,0.001972749,0.0003182626,0.00009585128,0.0003607812,0.0002848051,0.2448919,0.2144403,0.1677973,0.00457986,0.3646807],"study_design_scores_gemma":[0.00000942708,0.00006903524,0.0001667801,0.00001089936,0.000007869607,0.0001429752,0.00001210421,0.9747368,0.01270413,0.01027888,0.001846176,0.00001486618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01098356,0.0001280563,0.9867247,0.0001133368,0.00002396652,0.00003659453,0.00001709043,0.0002274049,0.001745166],"genre_scores_gemma":[0.4705259,0.0002862088,0.5245621,0.0001985479,0.00009915742,0.0001463441,0.00007616852,0.00003620422,0.004069382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001119305,"threshold_uncertainty_score":0.004012406,"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."}}