{"id":"W3012245672","doi":"10.1109/access.2020.2979376","title":"Phase Noise Compensation for CFBMC–OQAM Systems Under Imperfect Channel Estimation","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Computer science; Imperfect; Compensation (psychology); Phase noise; Noise (video); Channel (broadcasting); Control theory (sociology); Estimation; Electronic engineering; Telecommunications; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005023046,0.0004592374,0.0003052599,0.0002962739,0.0003500102,0.0003771373,0.0002811323,0.0004898167,0.0004214017],"category_scores_gemma":[0.00233813,0.0001280359,0.0001729945,0.0003372458,0.0003835052,0.0005290028,0.0003452224,0.0003620989,0.0001261262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002558615,"about_ca_system_score_gemma":0.0006664117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001927207,"about_ca_topic_score_gemma":0.00313687,"domain_scores_codex":[0.9995908,0.00009002534,0.00001900296,0.00006200328,0.0001866427,0.00005164654],"domain_scores_gemma":[0.9994499,0.0002524915,0.00009656431,0.00005363441,0.0001333448,0.00001410778],"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":[0.0006246367,0.00009579161,0.004958176,0.0002511927,0.00007984537,0.000352613,0.0003490351,0.4361111,0.1498087,0.01323737,0.000911057,0.3932205],"study_design_scores_gemma":[0.00001457123,0.0001489819,0.001232755,0.00001806332,0.00001917944,0.0001904977,0.00003470411,0.9648358,0.03124921,0.001123252,0.00111621,0.00001685307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09834346,0.0006182566,0.8985631,0.0001375648,0.00007125104,0.00002913209,0.0000197974,0.0001876109,0.002029841],"genre_scores_gemma":[0.8681654,0.0004367102,0.1300113,0.00005651899,0.00004082274,0.00002286963,0.0000443114,0.00001658344,0.001205627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001927207,"threshold_uncertainty_score":0.003831983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06974015376024281,"score_gpt":0.3338646277603895,"score_spread":0.2641244740001467,"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."}}