{"id":"W2325147653","doi":"10.1109/twc.2016.2548459","title":"Online Power Control Optimization for Wireless Transmission with Energy Harvesting and Storage","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Fading; Lyapunov optimization; Online algorithm; Transmission (telecommunications); Energy harvesting; Power control; Wireless; Channel state information; Optimization problem","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000149679,0.0003412531,0.0003306503,0.0001957149,0.0005295333,0.00006747141,0.0004838661,0.0001961309,0.00002470952],"category_scores_gemma":[0.000005803735,0.0002726006,0.00008150512,0.0003092916,0.000258403,0.0003830109,0.00000345896,0.0002709011,0.000001424214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001194622,"about_ca_system_score_gemma":0.00004483808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000363648,"about_ca_topic_score_gemma":0.00028599,"domain_scores_codex":[0.9985701,0.0001204674,0.0004205126,0.0003280832,0.0001853262,0.0003754947],"domain_scores_gemma":[0.9974066,0.001104213,0.00009600825,0.001037335,0.0001679533,0.000187871],"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":[0.0000644957,0.0001796843,0.00001246605,0.00003496338,0.0001129292,0.000001025362,0.000151974,0.8568724,0.004792011,0.0009115639,0.00003693366,0.1368295],"study_design_scores_gemma":[0.00211626,0.0001703331,0.00005731058,0.0006237932,0.00009685731,0.00001784069,0.00006132087,0.990687,0.003731323,0.00003564383,0.001934645,0.0004676985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02142421,0.0002451621,0.9761297,0.0006681724,0.0001516115,0.0003048215,0.0001771118,0.0007122704,0.0001869288],"genre_scores_gemma":[0.963744,0.001548239,0.0338314,0.00009105693,0.00003416404,0.0002578064,0.00003220542,0.0001439672,0.0003171118],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9423198,"threshold_uncertainty_score":0.9999726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347545075589102,"score_gpt":0.2225363971483877,"score_spread":0.2090609463924967,"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."}}