{"id":"W2783298029","doi":"10.1186/s13634-017-0526-4","title":"Joint frequency offset, time offset, and channel estimation for OFDM/OQAM systems","year":2018,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal frequency-division multiplexing; Cramér–Rao bound; Estimator; Computer science; Carrier frequency offset; Algorithm; Upper and lower bounds; Joint (building); Frequency offset; Channel (broadcasting); Estimation theory; Telecommunications; Mathematics; Statistics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005419416,0.0002378601,0.0003017424,0.0003294743,0.0002853416,0.0001959174,0.0001400714,0.00008778799,0.00002708592],"category_scores_gemma":[0.0001271316,0.0002199412,0.00004791497,0.0002722495,0.0001160176,0.001321148,0.00001366382,0.0003662498,0.00003175299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002046682,"about_ca_system_score_gemma":0.00004599576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.405885e-7,"about_ca_topic_score_gemma":0.000001109893,"domain_scores_codex":[0.9984616,0.00004958104,0.0006064499,0.0002317436,0.0002976334,0.0003530136],"domain_scores_gemma":[0.9992616,0.00008054084,0.0002412981,0.00009530062,0.0001893932,0.0001319215],"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":[0.0002335891,0.0001515076,0.0003281226,0.001630617,0.00006619161,0.00004578804,0.001734501,0.5673352,0.03896215,0.0006566483,0.002433004,0.3864227],"study_design_scores_gemma":[0.0008217524,0.0003988885,0.000242569,0.00209411,0.00002090681,0.0006617233,0.0002337358,0.9804835,0.004842904,0.006508513,0.003258784,0.000432627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1724068,0.04042555,0.7695062,0.0004867286,0.004284573,0.001135944,0.00003633365,0.0005947351,0.01112312],"genre_scores_gemma":[0.9911852,0.0002809527,0.007209755,0.00004540298,0.001088059,0.0000321671,0.000004067716,0.00005496734,0.00009945462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8187783,"threshold_uncertainty_score":0.8968941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632533164002134,"score_gpt":0.2714950399432098,"score_spread":0.2551697083031884,"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."}}