{"id":"W2808835896","doi":"10.1109/tvt.2018.2848963","title":"Resource Allocation in SWIPT Networks Under a Nonlinear Energy Harvesting Model: Power Efficiency, User Fairness, and Channel Nonreciprocity","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommunications link; Computer science; Resource allocation; Channel state information; Base station; Mathematical optimization; Maximum power transfer theorem; Channel (broadcasting); Resource management (computing); Wireless; Electronic engineering; Power (physics); Computer network; Engineering; Telecommunications; Mathematics","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":[0.0002344534,0.0003304382,0.0003057953,0.0005358935,0.0002212488,0.00004273622,0.0002822794,0.0007263438,0.000006377048],"category_scores_gemma":[0.00001420256,0.0003675484,0.00005658289,0.001077693,0.0003560938,0.0001672108,0.00000651558,0.0007486987,0.000006047417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001438412,"about_ca_system_score_gemma":0.00002847982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007487649,"about_ca_topic_score_gemma":0.0008937558,"domain_scores_codex":[0.9983028,0.00004982931,0.0003867073,0.0005139986,0.0001774541,0.0005691636],"domain_scores_gemma":[0.999199,0.00008650411,0.00005973448,0.0004771232,0.00009266136,0.00008501712],"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":[0.00001391853,0.00007610359,0.00002029232,0.00001187221,0.00003078026,0.000007972266,0.00007495447,0.9911759,0.002067249,0.0009426462,0.00002311365,0.005555186],"study_design_scores_gemma":[0.0004045669,0.0001005457,0.00004480116,0.0001395037,0.00002087988,0.00003461556,0.00006995485,0.9806416,0.01731003,0.0003375894,0.0005424638,0.0003534216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2161555,0.0001817262,0.781956,0.0002052095,0.0002246475,0.0001128986,0.000002892957,0.0009314208,0.0002296423],"genre_scores_gemma":[0.9946939,0.00009580849,0.004634806,0.0001520292,0.00007212929,0.0001053736,0.000005277948,0.00009911164,0.0001415302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7785384,"threshold_uncertainty_score":0.9998776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00783345160675796,"score_gpt":0.2035513164046251,"score_spread":0.1957178647978671,"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."}}