{"id":"W2940796154","doi":"10.1109/tbc.2019.2909190","title":"A Hybrid PAPR Reduction Scheme for OFDM Systems Using Perfect Sequences","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal frequency-division multiplexing; Reduction (mathematics); Quadrature amplitude modulation; Phase-shift keying; QAM; Algorithm; Mathematics; Modulation (music); Computational complexity theory; Multiplexing; Electronic engineering; Topology (electrical circuits); Computer science; Bit error rate; Channel (broadcasting); Telecommunications; Decoding methods; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002384984,0.0002678352,0.0002789986,0.0002678297,0.0002700755,0.0001005251,0.0001348674,0.0001020329,0.00007028528],"category_scores_gemma":[0.000009839148,0.000299503,0.0001637202,0.0002640933,0.00004223661,0.0004087394,8.186877e-7,0.0003197288,0.00009864578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003378456,"about_ca_system_score_gemma":0.00004657008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006370123,"about_ca_topic_score_gemma":9.12663e-7,"domain_scores_codex":[0.9985393,0.00004013624,0.0003993528,0.0003642841,0.0002366772,0.0004202503],"domain_scores_gemma":[0.9993274,0.0001088366,0.0000810451,0.0002939048,0.00009506518,0.00009376738],"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":[0.00001798351,0.00001862643,0.000008168123,0.0002131555,0.00006319761,0.000001067596,0.0001063093,0.725399,0.270003,0.00001316611,0.00007182395,0.004084511],"study_design_scores_gemma":[0.000518786,0.0001017252,0.00000567158,0.0003222773,0.00005720661,0.0004325115,0.0005070468,0.8400131,0.1563222,0.00002051315,0.001296007,0.0004029384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6052407,0.0001541906,0.3872014,0.00001253461,0.005597255,0.0005706141,0.00003485644,0.0005471917,0.0006413228],"genre_scores_gemma":[0.9924324,0.00002180862,0.006362047,0.000005862272,0.0003724881,0.0001269875,0.000003778046,0.00008860221,0.0005859831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3871918,"threshold_uncertainty_score":0.9999457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131996874426037,"score_gpt":0.2538892868095304,"score_spread":0.2225693180652701,"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."}}