{"id":"W2137463297","doi":"10.1109/vetecf.2005.1558433","title":"Reducing required power back-off of nonlinear amplifiers in serial modulation using SLM method","year":2006,"lang":"en","type":"article","venue":"","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Predistortion; Amplifier; Modulation (music); Orthogonal frequency-division multiplexing; Electronic engineering; Computer science; Power (physics); Nonlinear system; Telecommunications; Engineering; Bandwidth (computing); Acoustics; 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":[0.0002245555,0.0004934295,0.0002871211,0.0003280831,0.0002022027,0.0002748194,0.0002692159,0.0002596036,0.001920127],"category_scores_gemma":[0.001046123,0.0001373223,0.0001855039,0.0002661156,0.0002694337,0.0004488058,0.0001869698,0.0002318155,0.0004308805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001780177,"about_ca_system_score_gemma":0.0001733991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002155947,"about_ca_topic_score_gemma":0.0005256299,"domain_scores_codex":[0.9998327,0.00004765322,0.00001067255,0.00001796506,0.00008020797,0.00001069701],"domain_scores_gemma":[0.9994086,0.0003002103,0.0001185849,0.00008076971,0.00008101641,0.00001082447],"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":[0.000558771,0.0001326495,0.002985806,0.0003293726,0.00004402264,0.0002604296,0.0003688819,0.04311423,0.4068073,0.007085002,0.0006356408,0.5376779],"study_design_scores_gemma":[0.00007586357,0.00131716,0.003640773,0.00004524335,0.00006692383,0.00127099,0.0001157364,0.529693,0.4506159,0.003323293,0.009808343,0.0000267247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2056281,0.0003429425,0.7887092,0.000124605,0.00004620945,0.00005232563,0.00001533496,0.0004716989,0.004609671],"genre_scores_gemma":[0.6811377,0.0002952934,0.3142801,0.00004613592,0.00003715138,0.00005177183,0.00003368282,0.00005475516,0.004063503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001920127,"threshold_uncertainty_score":0.006423473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225866032762476,"score_gpt":0.2846336309384542,"score_spread":0.2620470276622066,"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."}}