{"id":"W2740690101","doi":"10.1109/icc.2017.7997233","title":"Optimal transmission policy in energy harvesting wireless communications: A learning approach","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Network packet; Wireless; Transmission (telecommunications); Channel (broadcasting); Transmitter; State space; Bellman equation; Energy (signal processing); Data transmission; Polynomial; Wireless network; Function (biology); Q-learning; Mathematical optimization; State (computer science); State-space representation; Algorithm; Computer network; Mathematics; Telecommunications; Artificial intelligence","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":[0.0002854236,0.0002077304,0.0002342438,0.0001666925,0.0004793559,0.0002363485,0.001097973,0.0001637418,0.000007657876],"category_scores_gemma":[0.000104917,0.0002172317,0.00005184148,0.0001686489,0.0001336182,0.0004100463,0.0001927451,0.000514592,0.000003789143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009635159,"about_ca_system_score_gemma":0.00003393418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002232475,"about_ca_topic_score_gemma":0.0002402815,"domain_scores_codex":[0.9988259,0.00008464533,0.0003192663,0.0002231908,0.0001402005,0.0004067796],"domain_scores_gemma":[0.9986374,0.0001161443,0.00006554466,0.001044575,0.0000245241,0.0001118435],"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":[0.000002551037,0.00002527664,0.001526768,0.00003361489,0.00001073737,0.000003184446,0.0001883096,0.8755924,0.0008374981,0.0106899,0.00002554871,0.1110642],"study_design_scores_gemma":[0.0003079155,0.000009971419,0.002331467,0.0001744301,0.000003979037,0.00001003155,0.00005299097,0.9891241,0.0005450869,0.00004905803,0.007138196,0.000252797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.195864,0.00133606,0.5057369,0.0003575255,0.0001347393,0.0001461557,0.000001111531,0.001546278,0.2948772],"genre_scores_gemma":[0.9357983,0.0008337225,0.0616345,0.00002283451,0.000137584,0.00005172603,0.00001791518,0.00006926731,0.001434098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7399344,"threshold_uncertainty_score":0.8858452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02212062223391874,"score_gpt":0.2557013023525555,"score_spread":0.2335806801186368,"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."}}