{"id":"W2154188754","doi":"10.1109/tcomm.2008.060106","title":"Cross-Layer Rate and Power Adaptation Strategies for IR-HARQ Systems over Fading Channels with Memory: A SMDP-Based Approach","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hybrid automatic repeat request; Computer science; Fading; Rayleigh fading; Markov decision process; Link adaptation; Physical layer; Transmitter; Automatic repeat request; Block Error Rate; Markov process; Telecommunications link; Channel (broadcasting); Computer network; Wireless; Telecommunications; Mathematics","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":[0.0001204275,0.0002270935,0.0002113194,0.0001752766,0.0007170739,0.0001224114,0.0002386554,0.000107958,0.000008320765],"category_scores_gemma":[0.000002799213,0.000231969,0.00005258744,0.0003540636,0.0001742294,0.0005882479,0.000001871791,0.0002538578,0.000002944626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000100786,"about_ca_system_score_gemma":0.00006395461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002832398,"about_ca_topic_score_gemma":0.00003315459,"domain_scores_codex":[0.9990731,0.00006978427,0.000273133,0.0002214554,0.0001246199,0.0002379003],"domain_scores_gemma":[0.9986554,0.0002868609,0.00007425569,0.0007623399,0.0001471006,0.00007397616],"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":[0.00004410764,0.00007636764,0.000007349577,0.00005081417,0.00005796046,3.4812e-7,0.0009519756,0.9976833,0.0004007257,0.0003330805,0.00002839604,0.0003655305],"study_design_scores_gemma":[0.0009682403,0.00007244838,0.00006921456,0.00006317117,0.00003867687,0.00001120365,0.0005792136,0.9969729,0.0006599796,0.00002964339,0.0002712233,0.000264047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01822933,0.0003474182,0.9790806,0.00004160878,0.0001992306,0.0008297524,0.00007003823,0.0004271583,0.0007748894],"genre_scores_gemma":[0.9659235,0.0003312511,0.03244621,0.00002544133,0.0000197261,0.0009522229,0.00006194915,0.00008060203,0.0001591017],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9476942,"threshold_uncertainty_score":0.9459422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04680008209293217,"score_gpt":0.2642501974351741,"score_spread":0.2174501153422419,"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."}}