{"id":"W2139189762","doi":"10.1109/glocom.2007.294","title":"On an Improved Markov Chain Model of the Rayleigh Fading Channel","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fading; Markov chain; Rayleigh fading; Autocorrelation; Channel state information; Fading distribution; Markov model; Computer science; State space; Statistical physics; Algorithm; Markov process; Channel (broadcasting); Variable-order Markov model; Rayleigh scattering; Mathematics; Statistics; Telecommunications; Physics; Optics","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":[0.000213514,0.00008721089,0.00008914314,0.0000598061,0.00003902546,0.000003802841,0.0003443348,0.00005465414,0.000007864558],"category_scores_gemma":[0.00001672464,0.00006632309,0.00003415093,0.0001125719,0.00002513372,0.00008884438,0.00005288449,0.0001388805,9.334585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004587614,"about_ca_system_score_gemma":0.000003957958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007183646,"about_ca_topic_score_gemma":0.00002426153,"domain_scores_codex":[0.999527,0.00001094576,0.0001672357,0.00007629221,0.00007714402,0.0001413746],"domain_scores_gemma":[0.9992541,0.00005250575,0.00003213095,0.0006049348,0.00002540443,0.00003092743],"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":[0.00002057105,0.00005778084,0.00002251801,0.00003599824,0.00001594675,1.729226e-7,0.0006347731,0.4103933,0.5187903,0.04256207,0.0001383773,0.02732823],"study_design_scores_gemma":[0.00005789208,0.00001124799,0.00004691823,0.00001138076,0.000001018508,2.013454e-7,0.00003840132,0.642094,0.354096,0.003565982,0.00001549062,0.00006150248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1047909,0.00002666874,0.8763992,0.00003831817,0.00004162069,0.0001775441,0.000002785189,0.0005447858,0.01797815],"genre_scores_gemma":[0.9766377,0.00002692949,0.02299289,0.00007230423,0.00001006035,0.00001125249,0.000001532854,0.00002673501,0.0002206157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8718468,"threshold_uncertainty_score":0.2704577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444754435668161,"score_gpt":0.2467487837659549,"score_spread":0.2323012394092733,"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."}}