{"id":"W1600673066","doi":"10.1109/icc.2015.7248304","title":"Optimal power control for energy harvesting cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Energy harvesting; Mathematical optimization; Computer science; Recursion (computer science); Throughput; Power control; Lift (data mining); Interference (communication); Energy (signal processing); Computation; Optimal control; Efficient energy use; Maximization; Optimization problem; Power (physics); Wireless; Algorithm; Channel (broadcasting); Mathematics; Computer network; Telecommunications; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001017586,0.0007931174,0.0007818748,0.0003780536,0.0003173862,0.001053522,0.0006853777,0.0006070296,0.001396177],"category_scores_gemma":[0.002296375,0.0004511515,0.0003370272,0.0005334424,0.001260277,0.0007811036,0.0009941468,0.0009226706,0.0002177638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008932282,"about_ca_system_score_gemma":0.0009325792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001657488,"about_ca_topic_score_gemma":0.001368328,"domain_scores_codex":[0.9996194,0.0001487747,0.0000135222,0.00007091188,0.00009077018,0.0000566058],"domain_scores_gemma":[0.9994366,0.0004052604,0.00005790989,0.00002065851,0.0000616834,0.0000178732],"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":[0.00004752854,0.00002731782,0.0001090159,0.00005909953,0.00001193113,0.00003459401,0.00005010525,0.9501799,0.001548793,0.0271602,0.0006823844,0.02008906],"study_design_scores_gemma":[0.000005572307,0.00001239758,0.00001460449,0.000003026434,0.00000162831,0.000003984977,0.000005300121,0.9927865,0.0001713133,0.006818412,0.0001752336,0.0000020036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009171518,0.0002517204,0.987268,0.0001354159,0.00003555652,0.00002172093,0.00001487754,0.00008060966,0.003020626],"genre_scores_gemma":[0.8951064,0.0005484294,0.1006076,0.0001149872,0.00005459832,0.000129077,0.00004140467,0.00006997565,0.00332739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001657488,"threshold_uncertainty_score":0.006480932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426114246273253,"score_gpt":0.214952109662025,"score_spread":0.2006909671992925,"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."}}