{"id":"W4404628181","doi":"10.1109/tce.2024.3504958","title":"PABSO-DRL: Power and Beam Self-Optimization Scheme for Multiple Slices in MU-MISO Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Power (physics); Beam (structure); Scheme (mathematics); Physics; Electrical engineering; Computer science; Electronic engineering; Engineering; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008404781,0.0007843419,0.0007170588,0.0002298045,0.0004616705,0.0005458975,0.001153818,0.0006234893,0.001785192],"category_scores_gemma":[0.00140875,0.0002508764,0.0003197393,0.000240903,0.000642113,0.0007958819,0.0010681,0.0008864609,0.0002589947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006246375,"about_ca_system_score_gemma":0.0008518152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003580103,"about_ca_topic_score_gemma":0.004049854,"domain_scores_codex":[0.9996098,0.0001244814,0.00001899669,0.00007123301,0.00009560944,0.00007999889],"domain_scores_gemma":[0.9995186,0.0001983886,0.0000830196,0.00004486403,0.0001012605,0.00005379983],"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":[0.0001763345,0.00006255141,0.0008455755,0.00006750564,0.0000363907,0.0001083206,0.0001120311,0.9157788,0.004960374,0.007811838,0.001761216,0.06827901],"study_design_scores_gemma":[0.00001133297,0.00004901655,0.00005902435,0.000003551706,0.000004566307,0.00001712533,0.000008143747,0.998041,0.0004698179,0.001030665,0.000301757,0.000004018646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04104855,0.0006166985,0.9531677,0.000293355,0.00007738208,0.00005909046,0.00003919237,0.000505194,0.004192838],"genre_scores_gemma":[0.9501307,0.0001351728,0.04756512,0.0001806559,0.0000271271,0.00005557188,0.00003788257,0.00002850802,0.001839234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003580103,"threshold_uncertainty_score":0.007118523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006852289273417377,"score_gpt":0.2067034991573269,"score_spread":0.1998512098839095,"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."}}