{"id":"W2135802165","doi":"10.1109/icc.2005.1494859","title":"Coding rate adaptation for hybrid ARQ systems over time varying fading channels with partially observable state","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partially observable Markov decision process; Computer science; Fading; Automatic repeat request; Markov process; Markov decision process; Network packet; Channel (broadcasting); Coding (social sciences); Markov chain; Observable; Heuristic; Channel state information; Buffer overflow; Algorithm; Mathematical optimization; Wireless; Markov model; Computer network; Hybrid automatic repeat request; Mathematics; Telecommunications link; Telecommunications; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001892636,0.0007542141,0.0008053845,0.000351913,0.000296984,0.001004491,0.00073254,0.0007200165,0.0009479084],"category_scores_gemma":[0.003232385,0.0004264686,0.000346268,0.0004554279,0.001246145,0.000901588,0.0009941567,0.0007890945,0.0001003333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134593,"about_ca_system_score_gemma":0.001037419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006002426,"about_ca_topic_score_gemma":0.003009565,"domain_scores_codex":[0.9992893,0.0002993126,0.0000210769,0.0001113732,0.000139965,0.0001389396],"domain_scores_gemma":[0.9976997,0.001691207,0.0002725163,0.00008084411,0.0001907888,0.00006502626],"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":[0.00002453822,0.000008738713,0.0000963469,0.00001232411,0.000008619583,0.00002267908,0.00001934714,0.9940037,0.0006486553,0.002987832,0.00007165195,0.002095617],"study_design_scores_gemma":[0.000007898019,0.00001113245,0.00004129397,0.000001452127,0.000003026055,0.000003424766,0.000004850059,0.9983678,0.0001873659,0.001333624,0.00003583523,0.000002282087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09396392,0.0003426467,0.9030507,0.0003373187,0.00002822324,0.00004819002,0.0000576884,0.0001560776,0.002015244],"genre_scores_gemma":[0.9816515,0.0001296661,0.01737648,0.00003690613,0.00001218616,0.00005343298,0.00002154733,0.00001429866,0.0007039956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006002426,"threshold_uncertainty_score":0.011935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710455108178294,"score_gpt":0.2067358041574705,"score_spread":0.1896312530756875,"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."}}