{"id":"W2130258422","doi":"10.1109/tbc.2003.817096","title":"Fast simulation of diversity nakagami fading channels using finite-state markov models","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Patras","keywords":"Nakagami distribution; Fading; Computer science; Bit error rate; Markov chain; Waveform; Diversity combining; Diversity gain; Signal-to-noise ratio (imaging); Algorithm; Channel (broadcasting); Electronic engineering; Markov process; Channel state information; Envelope (radar); Simulation; Statistics; Telecommunications; Mathematics; Engineering; Wireless","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":[0.0001650422,0.0001911053,0.0002122815,0.0002721558,0.0003538828,0.00001824738,0.000172929,0.00008228718,0.00002188459],"category_scores_gemma":[0.00001766265,0.0002407367,0.00009086426,0.0003967056,0.00004400242,0.0004821072,0.000005038337,0.0002865253,0.000002821298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001554367,"about_ca_system_score_gemma":0.00001234619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002244698,"about_ca_topic_score_gemma":0.000004296568,"domain_scores_codex":[0.9989837,0.00006579841,0.000339456,0.0001793751,0.000187646,0.0002439884],"domain_scores_gemma":[0.999076,0.0003345715,0.00009789992,0.0003340316,0.00009794432,0.00005952051],"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":[0.000007940204,0.0000293015,0.000007304986,0.00004521845,0.00002371146,0.000001442066,0.0007980499,0.9705167,0.005433182,0.00001615761,6.320779e-7,0.0231204],"study_design_scores_gemma":[0.0001830761,0.00001794669,0.000002427984,0.0001249622,0.00001762936,0.00000350987,0.00009873346,0.9047657,0.09413193,0.0004491508,0.00001422238,0.0001907126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1097442,0.00004600618,0.8886738,0.000001836452,0.0001912443,0.0001704484,0.00002135159,0.0004463669,0.0007047731],"genre_scores_gemma":[0.9657385,0.0000631954,0.03408712,0.000008259767,0.000008381993,0.000008938051,0.000001529982,0.00004636836,0.0000377116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8559943,"threshold_uncertainty_score":0.9816958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06288957464712279,"score_gpt":0.2733565058684727,"score_spread":0.2104669312213499,"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."}}