{"id":"W3044698225","doi":"10.1109/lcomm.2020.3010698","title":"RFF Based Detection for SCMA in Presence of PA Nonlinearity","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Codebook; Computer science; Robustness (evolution); Message passing; Bit error rate; Multiuser detection; Algorithm; Nonlinear system; Amplifier; Random access; Coding (social sciences); Computer engineering; Decoding methods; Code division multiple access; Bandwidth (computing); Distributed computing; Telecommunications; Computer network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009830188,0.00009341996,0.0001471124,0.0001089377,0.00006195995,0.000009104015,0.00119156,0.0000623599,0.000001882159],"category_scores_gemma":[0.00021443,0.0001129374,0.00004693183,0.0004286105,0.0001642251,0.0001346429,0.0001004083,0.0002734975,0.000003269915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005499316,"about_ca_system_score_gemma":0.00001065654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002325823,"about_ca_topic_score_gemma":0.0001661186,"domain_scores_codex":[0.9993666,0.00004875777,0.0002842488,0.0001046928,0.00006995626,0.0001257315],"domain_scores_gemma":[0.9979532,0.0004399458,0.00006487477,0.001468791,0.00004754581,0.00002568246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001761272,0.00005717969,0.0006541047,0.0001304796,0.000016601,2.030913e-7,0.0003128283,0.4453035,0.5272554,0.0002438404,0.000500883,0.02550738],"study_design_scores_gemma":[0.0003484324,0.00002016775,0.000715247,0.00003506315,0.000004433899,2.373204e-7,0.00009164916,0.8024027,0.1921672,0.0001101391,0.003975741,0.0001290314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1012948,0.0003870215,0.8837547,0.01328712,0.00005891908,0.0004758484,0.00004369985,0.000590398,0.0001074884],"genre_scores_gemma":[0.9009478,0.0001787839,0.09824449,0.0003832114,0.000008194247,0.0001975119,0.00002023805,0.00001927446,4.953811e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.799653,"threshold_uncertainty_score":0.4605455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04761774887965042,"score_gpt":0.2729470894435615,"score_spread":0.2253293405639111,"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."}}