{"id":"W2988549429","doi":"10.1109/lcomm.2019.2951404","title":"Channel Equalization and Detection With ELM-Based Regressors for OFDM Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Equalization (audio); Orthogonal frequency-division multiplexing; Computer science; Extreme learning machine; Benchmark (surveying); Computational complexity theory; Channel (broadcasting); Reduction (mathematics); Adaptability; Algorithm; Artificial intelligence; Artificial neural network; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002874531,0.00009003556,0.0001024077,0.0001167337,0.0002452463,0.000140502,0.0006739124,0.00003524594,4.296477e-7],"category_scores_gemma":[0.00001949052,0.00008006401,0.00002254122,0.0002237018,0.00004420398,0.0002037387,0.00005946709,0.00008987139,0.000009092171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003012226,"about_ca_system_score_gemma":0.00001671103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001248319,"about_ca_topic_score_gemma":0.0000200207,"domain_scores_codex":[0.9992884,0.000149226,0.0001332475,0.0001958222,0.00009742012,0.0001359321],"domain_scores_gemma":[0.9982731,0.0002202218,0.0001217036,0.001281559,0.0000663258,0.00003705936],"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":[0.0002049609,0.0005664416,0.008817604,0.001154751,0.0003148021,0.000002121167,0.008191682,0.6313941,0.14504,0.07491042,0.004555013,0.1248481],"study_design_scores_gemma":[0.0005576877,0.00009387168,0.000313246,0.00006786815,0.000008923605,0.000003874067,0.00002780972,0.989452,0.0008800584,0.00005634034,0.008394448,0.0001438753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0524051,0.0001322158,0.9409053,0.005692676,0.0001916584,0.0003671569,0.000002329549,0.0001557182,0.0001478012],"genre_scores_gemma":[0.9886585,0.000009852828,0.01029258,0.0007963129,0.00001971011,0.0001078368,0.00001425472,0.00001208286,0.00008886636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9362534,"threshold_uncertainty_score":0.3264916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0213201087626531,"score_gpt":0.2569837064503196,"score_spread":0.2356635976876665,"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."}}