{"id":"W4285292416","doi":"10.1109/access.2022.3188675","title":"Novel PAPR Reduction Algorithms for OFDM Signals","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal frequency-division multiplexing; Algorithm; Reduction (mathematics); Computer science; Clipping (morphology); SIGNAL (programming language); Time domain; Frequency domain; Channel (broadcasting); Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005827048,0.0009801829,0.0004917654,0.0008991135,0.0003668851,0.0007351405,0.0008726054,0.0005405706,0.002022788],"category_scores_gemma":[0.002162262,0.0003025583,0.0005181261,0.0006984756,0.0004334635,0.0009858542,0.0004735233,0.0009418937,0.001051109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003892644,"about_ca_system_score_gemma":0.0005011478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006441517,"about_ca_topic_score_gemma":0.0009949531,"domain_scores_codex":[0.999244,0.0001049372,0.00004910166,0.0001244125,0.0004362803,0.0000413485],"domain_scores_gemma":[0.9993047,0.0003000349,0.00007814333,0.0001017773,0.000200963,0.00001442089],"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":[0.0001320636,0.00008710435,0.0003741789,0.0002337725,0.00005035793,0.00009782222,0.0002038329,0.08274648,0.04973431,0.02656951,0.002621585,0.837149],"study_design_scores_gemma":[0.00004705444,0.0001812949,0.0006433721,0.00003686463,0.00004019625,0.0005859599,0.00004193839,0.9250126,0.04550384,0.01026122,0.01759783,0.00004791452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002352095,0.0002275946,0.9962087,0.0000270014,0.00003029925,0.00001932113,0.00001157713,0.0002740379,0.0008494937],"genre_scores_gemma":[0.06220878,0.0006073892,0.9334982,0.00006372901,0.0001339916,0.00008492141,0.0001131187,0.00009414101,0.003195661],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002022788,"threshold_uncertainty_score":0.006766856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602072461479402,"score_gpt":0.3202665978642618,"score_spread":0.2542458732494677,"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."}}