{"id":"W4318159243","doi":"10.1016/j.dsp.2023.103943","title":"Proportional fairness secrecy beamforming for massive MIMO-SWIPT systems with low-resolution ADCs","year":2023,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of British Columbia; Natural Science Foundation of Sichuan Province; China Postdoctoral Science Foundation","keywords":"Beamforming; MIMO; Computer science; Maximum power transfer theorem; Convex optimization; Maximization; Nakagami distribution; Wireless; Electronic engineering; Optimization problem; Mathematical optimization; Fading; Power (physics); Telecommunications; Algorithm; Mathematics; Regular polygon; Engineering; Decoding methods","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016644,0.0009548605,0.00105431,0.0003530737,0.0008256059,0.001668857,0.0009317345,0.0009015133,0.003609454],"category_scores_gemma":[0.004757439,0.0004979119,0.0003398614,0.0009890924,0.001046084,0.001694937,0.001416474,0.001005302,0.00096925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007388495,"about_ca_system_score_gemma":0.001120819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001188028,"about_ca_topic_score_gemma":0.002472563,"domain_scores_codex":[0.9986468,0.0003737904,0.00006356568,0.0002546027,0.0004582184,0.0002030264],"domain_scores_gemma":[0.9982567,0.001007674,0.0001305381,0.0002194277,0.0003365702,0.00004908343],"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":[0.0009903613,0.0002393497,0.001644049,0.0006043763,0.0001676565,0.0005671213,0.0004009269,0.5204993,0.08297577,0.1526546,0.005511553,0.2337449],"study_design_scores_gemma":[0.00002994133,0.0001044802,0.0002956653,0.00002233887,0.00002713841,0.0002409687,0.00004664202,0.9624838,0.009343483,0.02550996,0.001865393,0.00003023181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01502896,0.0005995003,0.9770069,0.0003126525,0.000119143,0.0000526848,0.00009186709,0.000167858,0.006620471],"genre_scores_gemma":[0.897114,0.0008015471,0.09288895,0.0002746577,0.0001787951,0.00009287178,0.00008968093,0.00004100398,0.008518453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003609454,"threshold_uncertainty_score":0.01207483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017596569447973,"score_gpt":0.2052450771553823,"score_spread":0.1950691114609025,"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."}}