{"id":"W4200200047","doi":"10.3390/s21248408","title":"Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Codebook; Reproducing kernel Hilbert space; Computer science; Bit error rate; Quantization (signal processing); Algorithm; Multiuser detection; Noma; Computer engineering; Detector; Electronic engineering; Mathematics; Telecommunications; Telecommunications link; Engineering; Hilbert space; Decoding methods","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":[0.002181351,0.0009360077,0.000740782,0.001294742,0.0003480357,0.001175932,0.0007757946,0.000895123,0.002211977],"category_scores_gemma":[0.01317888,0.0003286297,0.0005485924,0.0009962627,0.0008450493,0.001679911,0.0008703301,0.0008398127,0.0005492741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136186,"about_ca_system_score_gemma":0.0008938007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025813,"about_ca_topic_score_gemma":0.001519624,"domain_scores_codex":[0.9980832,0.0006266102,0.00004979806,0.0001402843,0.0009888352,0.0001112049],"domain_scores_gemma":[0.9884433,0.008658977,0.0005532259,0.0005444256,0.001718431,0.00008164177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004222767,0.0001663664,0.002053493,0.0008335729,0.0001709815,0.0002408455,0.0003716427,0.599744,0.03600919,0.1023195,0.001840837,0.2558272],"study_design_scores_gemma":[0.000004270934,0.00007438005,0.0003057733,0.00003344993,0.00001465212,0.0001508905,0.00002771595,0.9876882,0.006851261,0.00375191,0.001079991,0.00001746731],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03247073,0.004075047,0.9541961,0.000284669,0.00006207733,0.00006181538,0.00004174955,0.0003250167,0.008482873],"genre_scores_gemma":[0.7404939,0.00446382,0.2485555,0.0001431957,0.0001187289,0.0001136531,0.0001101654,0.0001406092,0.005860512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002211977,"threshold_uncertainty_score":0.01153624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05721576314701495,"score_gpt":0.3973488942491614,"score_spread":0.3401331311021465,"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."}}