{"id":"W2168330593","doi":"10.1109/vtcf.2006.427","title":"An OFDM Rayleigh Fading Channel Simulator","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Correlation function (quantum field theory); Rayleigh fading; Computer science; Fading; Algorithm; Orthogonal frequency-division multiplexing; Transformation (genetics); Correlation; Computation; Covariance matrix; Channel (broadcasting); Gaussian; Cross-correlation; Additive white Gaussian noise; Electronic engineering; Spectral density; Mathematics; Decoding methods; Statistics; Telecommunications; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009312692,0.0002565192,0.0002814046,0.0004042037,0.0001178713,0.00003526536,0.0008559115,0.0004249506,0.00001741752],"category_scores_gemma":[0.00001592684,0.0002849599,0.00004794419,0.0004946049,0.0002114544,0.0002609684,0.00005498958,0.0004752678,0.00004915013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008232792,"about_ca_system_score_gemma":0.00002033458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001584262,"about_ca_topic_score_gemma":0.00002205558,"domain_scores_codex":[0.9988462,0.00002757314,0.0003083656,0.0003007008,0.0001350999,0.0003820325],"domain_scores_gemma":[0.9985726,0.0000312361,0.00006508472,0.001142561,0.0001367855,0.00005169167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004573361,0.00009795288,0.001016456,0.00004468318,0.00003726288,0.00003593138,0.00006981965,0.2293768,0.7079414,0.04715605,0.0002421253,0.0139769],"study_design_scores_gemma":[0.0002155332,0.00004588606,0.0001516217,0.0000507417,0.00001019235,0.00001634642,0.00006618904,0.3100087,0.6604777,0.02599025,0.002569383,0.0003974221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5722437,0.0004472783,0.4203069,0.0001523899,0.0001131354,0.0002172183,0.000005716631,0.005675196,0.0008385147],"genre_scores_gemma":[0.9904001,0.0001274068,0.009173536,0.00002869706,0.00004499656,0.0001153484,0.0000192611,0.00005996792,0.0000306922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4181564,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054464313282745,"score_gpt":0.238541097823528,"score_spread":0.2279964546907006,"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."}}