{"id":"W3129505490","doi":"10.1109/access.2021.3060836","title":"A Joint Beamforming and Power-Splitter Optimization Technique for SWIPT in MISO-NOMA System","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; UK-India Education and Research Initiative","keywords":"Beamforming; Computer science; Maximum power transfer theorem; Splitter; Telecommunications link; Transmitter power output; Transmitter; Base station; Power budget; Convex optimization; Power (physics); Optimization problem; Energy harvesting; Mathematical optimization; Decoding methods; Energy (signal processing); Electronic engineering; Telecommunications; Algorithm; Power control; Mathematics; Channel (broadcasting); Regular polygon; 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":[0.0004625013,0.0009861548,0.0007400736,0.0002694329,0.0002978505,0.0004779313,0.0004673133,0.0004878718,0.001427955],"category_scores_gemma":[0.0005375161,0.0003202442,0.0006260352,0.0006642795,0.0004675129,0.0008633553,0.0006034885,0.0007547929,0.0005095814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002764529,"about_ca_system_score_gemma":0.0005340467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004982147,"about_ca_topic_score_gemma":0.0009349497,"domain_scores_codex":[0.9996608,0.0001072375,0.0000190786,0.00006601609,0.0001122059,0.00003461505],"domain_scores_gemma":[0.9998593,0.00005845524,0.00002589971,0.00001753891,0.0000295855,0.000009212165],"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":[0.0002090414,0.0001393457,0.0006242548,0.0002919066,0.0001620247,0.0003192248,0.0001792463,0.617529,0.09755152,0.05450092,0.003360111,0.2251334],"study_design_scores_gemma":[0.00001340255,0.0001450015,0.0001617329,0.00001169695,0.00002462308,0.0001679488,0.00002485926,0.9814194,0.007447406,0.008424961,0.00214306,0.00001575751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00403639,0.0002040853,0.9942199,0.00008115536,0.00002328613,0.00001693894,0.00002103706,0.00006531292,0.001331902],"genre_scores_gemma":[0.4386092,0.001263418,0.5528501,0.0002414405,0.0001344947,0.0001968125,0.0001112467,0.0000606312,0.006532717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001427955,"threshold_uncertainty_score":0.004776955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01647335644019799,"score_gpt":0.2352769192283195,"score_spread":0.2188035627881215,"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."}}