{"id":"W3083981910","doi":"10.1109/ojcoms.2020.3022316","title":"Performance Analysis and Resource Allocations for a WPCN With a New Nonlinear Energy Harvester Model","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Phase-shift keying; Computer science; Monte Carlo method; Bit error rate; Energy (signal processing); Energy harvesting; Keying; Beamforming; Antenna (radio); Wireless; Nonlinear system; Electronic engineering; Telecommunications; Control theory (sociology); Mathematics; Statistics; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135228,0.0007142077,0.0006977092,0.0003837713,0.0004273118,0.0009076854,0.000899084,0.0007799813,0.001506112],"category_scores_gemma":[0.002758336,0.0002193613,0.0003983853,0.0006433271,0.001080542,0.001465245,0.001012871,0.000693235,0.0002778627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143654,"about_ca_system_score_gemma":0.0008002879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003838718,"about_ca_topic_score_gemma":0.00229954,"domain_scores_codex":[0.9995211,0.0001515958,0.00001445684,0.00008459848,0.0001309163,0.00009733],"domain_scores_gemma":[0.9987057,0.0007452876,0.000157545,0.0001004958,0.0002499827,0.00004099125],"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":[0.0001087086,0.00003005388,0.0007672165,0.00006956659,0.00002218265,0.00009979924,0.00005054744,0.9728903,0.006264108,0.0103721,0.0002937381,0.009031609],"study_design_scores_gemma":[0.00000389678,0.00003880072,0.000113468,0.00000405116,0.000007780048,0.00002803921,0.00001632798,0.9977809,0.0007917468,0.001116956,0.00009275731,0.000005403079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2821133,0.0008995471,0.6969985,0.0006621191,0.00003406859,0.00008570627,0.0001433553,0.0002841941,0.01877924],"genre_scores_gemma":[0.9817373,0.0003679287,0.01522818,0.00004902222,0.00001054993,0.00004955753,0.0000434862,0.0000193136,0.002494759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003838718,"threshold_uncertainty_score":0.01042295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04505044684082082,"score_gpt":0.2522723771610175,"score_spread":0.2072219303201966,"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."}}