{"id":"W4312749337","doi":"10.1109/tvt.2022.3213811","title":"A Random Fourier Feature Based Receiver Detection for Enhanced BER Performance in Nonlinear PD-NOMA","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Noma; Nonlinear system; Computer science; Electronic engineering; Power (physics); Amplifier; Orthogonal frequency-division multiplexing; Fourier transform; Spectral efficiency; Frequency domain; Algorithm; Mathematics; Telecommunications; Engineering; Bandwidth (computing); Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001023617,0.0006967636,0.0006659641,0.0005404511,0.0004630467,0.0006399879,0.0009797744,0.001075039,0.001008993],"category_scores_gemma":[0.00342101,0.0002768132,0.000428825,0.0005313531,0.0007047447,0.0008434179,0.0008061838,0.0008076714,0.0005869365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004394415,"about_ca_system_score_gemma":0.0007589853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006839148,"about_ca_topic_score_gemma":0.001244121,"domain_scores_codex":[0.9993207,0.0002483413,0.00002840788,0.0001118736,0.0002243421,0.00006637993],"domain_scores_gemma":[0.9988981,0.0005729232,0.0001563512,0.0001396611,0.0001991312,0.00003375104],"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":[0.0007774042,0.0003600446,0.002815533,0.0004248268,0.000140947,0.0006642028,0.0003658262,0.357879,0.22497,0.05826814,0.002797309,0.3505369],"study_design_scores_gemma":[0.00001993028,0.0001287595,0.0002492895,0.00001206612,0.00001389172,0.0002792152,0.000009780398,0.9803481,0.01633133,0.001773579,0.0008115809,0.00002252859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02790357,0.000384991,0.9686759,0.0001764523,0.00006090373,0.0000474526,0.00002843865,0.000444964,0.002277427],"genre_scores_gemma":[0.5973995,0.0003421273,0.3992725,0.0001647061,0.00007417573,0.00008214179,0.00005594808,0.00003820892,0.002570762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001075039,"threshold_uncertainty_score":0.005413473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006831029644346908,"score_gpt":0.2103510388382815,"score_spread":0.2035200091939346,"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."}}