{"id":"W2096017667","doi":"10.1109/glocom.2006.610","title":"SPCp1-08: Adaptive Learning of Transmission Control Policies for MIMO Fading Channels under Delay Constraint","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fading; Markov decision process; Computer science; MIMO; Mathematical optimization; Transmission (telecommunications); Convergence (economics); Markov process; Constraint (computer-aided design); Q-learning; Transmitter; Channel (broadcasting); Power control; Reinforcement learning; Resource allocation; Wireless; Power (physics); Mathematics; Telecommunications; Computer network; Artificial intelligence; Statistics","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":[0.00008279132,0.0001660348,0.000257712,0.00007466569,0.0000828058,0.00001458215,0.00007587545,0.00009819042,0.00002474495],"category_scores_gemma":[0.000008617758,0.0001746027,0.0000832527,0.0001335551,0.00005455127,0.000109651,0.000006736829,0.0001161683,0.000002831653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008176102,"about_ca_system_score_gemma":0.0000140162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003866055,"about_ca_topic_score_gemma":0.000009642111,"domain_scores_codex":[0.9991475,0.00002275467,0.0002874755,0.0001415739,0.00009857823,0.0003020624],"domain_scores_gemma":[0.9995803,0.0001456014,0.00007459773,0.00008302121,0.00006976541,0.00004669901],"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":[0.00003039109,0.00001185615,0.0001015458,0.00003399815,0.00003131839,8.349965e-7,0.0001344382,0.9867941,0.003304661,0.004962086,0.0001944807,0.004400278],"study_design_scores_gemma":[0.001177061,0.00006518848,0.0002052966,0.00009301255,0.00002950641,0.000005269349,0.0001886966,0.9902669,0.004367312,0.001400572,0.001994364,0.0002068293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02174369,0.0005474622,0.9755318,0.00005906375,0.0001559561,0.000337242,0.00002532319,0.0002113786,0.001388056],"genre_scores_gemma":[0.9848933,0.000043461,0.01472379,0.00002324823,0.0001344556,0.00002253148,0.00003558671,0.00004156115,0.00008208824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9631496,"threshold_uncertainty_score":0.712009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00894306168979507,"score_gpt":0.2172338974107696,"score_spread":0.2082908357209746,"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."}}