{"id":"W2884968439","doi":"10.1109/tvt.2018.2855696","title":"BER Analysis of WFRFT Precoded OFDM and GFDM Waveforms With an Integer Time Offset","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Orthogonal frequency-division multiplexing; Offset (computer science); Waveform; Integer (computer science); Computer science; Electronic engineering; Telecommunications; Engineering","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":[0.000483442,0.0005387453,0.0003831866,0.0005840776,0.0002669015,0.0004165691,0.0003404542,0.0006613674,0.0009510103],"category_scores_gemma":[0.002728689,0.0001159579,0.0002203782,0.0006863108,0.0004356864,0.0008308835,0.000282764,0.0002711006,0.0002495307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006094661,"about_ca_system_score_gemma":0.0003133021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001374151,"about_ca_topic_score_gemma":0.001145616,"domain_scores_codex":[0.9994646,0.00008368672,0.00002348343,0.00007557703,0.00027185,0.00008077392],"domain_scores_gemma":[0.9983864,0.0006985497,0.0002768158,0.000151704,0.0004596443,0.00002688237],"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":[0.001651144,0.0001406236,0.01173635,0.0006712596,0.0002093193,0.001444884,0.0004920443,0.4061466,0.4222454,0.0228258,0.001001622,0.1314351],"study_design_scores_gemma":[0.00002308145,0.000285646,0.005578479,0.00003526701,0.00004778931,0.0005377179,0.000106583,0.866742,0.1226793,0.002726837,0.00118876,0.00004854725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8641818,0.001553202,0.1259965,0.0001853316,0.00005486376,0.0000224795,0.0001908885,0.0003289614,0.007485911],"genre_scores_gemma":[0.9783275,0.000502835,0.01967601,0.00003684656,0.00001182943,0.00001261055,0.000104323,0.0000241382,0.001303997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001374151,"threshold_uncertainty_score":0.004422069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004780642026876312,"score_gpt":0.2071504399902694,"score_spread":0.2023697979633931,"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."}}