{"id":"W2124715093","doi":"10.1109/tsp.2009.2027735","title":"Monotonicity of Constrained Optimal Transmission Policies in Correlated Fading Channels With ARQ","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov decision process; Monotonic function; Monotone polygon; Mathematical optimization; Fading; Scheduling (production processes); Dynamic programming; Channel state information; Computer science; Channel (broadcasting); Mathematics; Markov process; Wireless; Computer network; Statistics; 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":[0.00007272737,0.0002171397,0.0002705127,0.0002804262,0.0001030513,0.00002573418,0.00008692844,0.0001226198,0.00002139597],"category_scores_gemma":[6.453487e-7,0.0002097972,0.00004490354,0.000704474,0.00006712544,0.0003721298,2.189908e-7,0.0003405249,9.832938e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008950223,"about_ca_system_score_gemma":0.00004231016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000446469,"about_ca_topic_score_gemma":0.000002821759,"domain_scores_codex":[0.9989676,0.00002040363,0.0003474073,0.0001937714,0.0001784304,0.0002924085],"domain_scores_gemma":[0.9996805,0.00004249577,0.00006709798,0.00008207925,0.00005830306,0.00006956961],"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":[0.0001153647,0.00006741274,0.000004960638,0.00004659022,0.00001056659,0.00000458182,0.0009037179,0.8830137,0.02543915,0.000005188559,8.74813e-7,0.09038789],"study_design_scores_gemma":[0.000714832,0.0001561741,0.00004441006,0.0005304317,0.00001958609,0.00001284106,0.0001183013,0.8594714,0.1386957,0.00002945615,0.000005185587,0.0002016652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1816964,0.00009236515,0.8174887,0.00003452302,0.00003489549,0.0001877538,0.000003442331,0.0002119602,0.0002498683],"genre_scores_gemma":[0.9868864,0.00005753773,0.01295472,0.00002101633,0.00001563615,0.00001252301,0.000002962546,0.00003372535,0.00001542333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.80519,"threshold_uncertainty_score":0.8555283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00890911092742783,"score_gpt":0.2245642888156421,"score_spread":0.2156551778882142,"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."}}