{"id":"W4319662280","doi":"10.22541/au.167597462.26786780/v1","title":"An Innovative Digital Equalizer for Wireless Communications","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Predistortion; Electronic engineering; Computer science; Amplifier; Finite impulse response; Frequency response; Nonlinear distortion; Frequency band; Adaptive equalizer; Radio frequency; Nonlinear system; Wireless; Equalization (audio); Electrical engineering; Engineering; Channel (broadcasting); Telecommunications; Bandwidth (computing); Physics","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":[0.0002883597,0.0004600008,0.0002603847,0.0003969324,0.0002481963,0.0005579458,0.0005206403,0.0006898065,0.004116222],"category_scores_gemma":[0.0005702048,0.0001522241,0.000256824,0.0002854377,0.0002729498,0.0007555201,0.0003759984,0.0005908799,0.001305642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003791472,"about_ca_system_score_gemma":0.0002850759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002686298,"about_ca_topic_score_gemma":0.0005075858,"domain_scores_codex":[0.9997305,0.00004702276,0.00001315934,0.00006483048,0.0001207342,0.00002372198],"domain_scores_gemma":[0.9998552,0.0000479271,0.0000130066,0.00002154832,0.00005450845,0.000007858882],"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":[0.0003681796,0.0001142407,0.0005104419,0.0002900993,0.0000724522,0.0003360094,0.0001139333,0.01825082,0.525263,0.04338509,0.004194046,0.4071017],"study_design_scores_gemma":[0.000135765,0.0008258682,0.001303124,0.00007476921,0.0001484417,0.001946788,0.00004198375,0.2906087,0.5575688,0.006336166,0.1409422,0.00006742067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01554654,0.0008345585,0.9696457,0.000267201,0.0002866968,0.000114118,0.00006846266,0.001217914,0.0120188],"genre_scores_gemma":[0.38591,0.00123923,0.5749424,0.0004885192,0.0002447041,0.0001627088,0.0002018129,0.0001089121,0.03670168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004116222,"threshold_uncertainty_score":0.0137701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415796382059004,"score_gpt":0.4050718807453504,"score_spread":0.26349224253945,"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."}}